ZooKeeper will be completely gone in the next major Apache Kafka release (Kafka 4), and replaced by Kafka Raft, or KRaft mode. Many developers are excited about this change, but it will impact teams currently running Kafka with ZooKeeper who need to determine an upgrade path once ZooKeeper is no longer supported.

In this blog, our expert explains what KRaft mode is and how Raft implementations differ from ZooKeeper-based deployments, what to consider when planning your KRaft migration, and how your environment will look different when you’re running Kafka without ZooKeeper.

Note: This blog was originally published in 2022 and was updated and revised in 2025 to reflect the latest developments.

What Is Kafka Raft (KRaft) Mode?

Kafka Raft (which loosely stands for Reliable, Replicated, Redundant, And Fault Tolerant) or KRaft, is Kafka’s implementation of the Raft consensus algorithm.

Created as an alternative to the Paxos family of algorithms, the Raft Consensus protocol is meant to be a simpler consensus implementation with the goal of being easier to understand than Paxos. Both Paxos and Raft operate in similar manner under normal stable operating conditions, and both protocols accomplish the following:

• Leader writes operation to its log and requests following servers to do the same thing
• The operation is marked as “commited” once a majority of servers acknowledge the operation

This results in a consensus-based change to the state machine, or in this specific case, the Kafka cluster.

The main difference between Raft and Paxos, however, is when operations are not normal and new leader must be elected. Both algorithms will guarantee that the new leader’s log will contain the most up-to-date commits, but how they accomplish this process differs.

In Paxos, the leader election contains not only the proposal and subsequent vote, but also must contain any missing log entries the candidate is missing. Followers in Paxos implementations can vote for any candidate and once the candidate is elected as leader, the new leader will utilize this data to update its log to maintain currency.

In Raft, on the other hand, followers will only vote for a candidate if the candidate’s log is the at least as up to date as the follower’s log. This means only the most up-to-date candidate will be elected as leader. Ultimately, both protocols are remarkably similar in their approach to solving the consensus problem. However, with Raft making some base assumptions about the data, namely the order of commits in the log, we can see improvements in election efficiency in Raft.

What does this mean in regards to Kafka? From the protocol side of things, not much. ZooKeeper utilizes a proprietary consensus protocol called ZAB (ZooKeeper Atomic Broadcast) that is much more focused on total ordering of commits to the change log. This focus on commit order makes Raft consensus fit quite well into the Kafka ecosystem.

That said, changes from an infrastructure perspective will be quite noticeable. With each broker having the Kraft logic incorporated into the base code, ZooKeeper nodes will no longer be part of the Kafka infrastructure. Note that this doesn’t necessarily mean less servers in the production environment — more on that later.

Why Is Kafka Raft Replacing ZooKeeper?

To understand why the Kafka community leadership decided to make this move away from ZooKeeper, we can look directly at KIP-500 for their reasoning. In short, this move was meant to reduce complexity and handle cluster metadata in a more robust fashion. Removing the requirement for ZooKeeper means there is no longer a need to deploy two distinctly different distributed systems. ZooKeeper has different deployment patterns, management tools, and configuration syntax when compared to Kafka. Unifying the functionality to single system will reduce configuration errors and overall operational complexity.

In addition to simpler operations, treating the metadata as its own event stream means that a single number, an offset, can be used to describe a cluster member’s position and be quickly brought up to date. This in effect applies the same principles used for producers and consumers to the Kafka cluster members themselves.

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KRaft vs. ZooKeeper

In a ZooKeeper-based Kafka deployment, the cluster consists of several broker nodes and a quorum of ZooKeeper nodes. In this environment, each change to the cluster metadata is treated as an isolated event, with no relationship to previous or future events. When state changes are pushed out to the cluster from the cluster controller, a.k.a. the broker in charge of tracking/electing partition leadership, there is potential for some brokers to not receive all updates, or for stale updates to create race conditions as we’ve seen in some larger Kafka installations. Ultimately, these failure points have the potential to leave brokers in divergent states.

While not entirely accurate, as all broker nodes can (and do) talk to ZooKeeper, the image below is a basic example of what that looks like:

In contrast, the metadata in KRaft is stored within Kafka itself and ZooKeeper is effectively replaced by a quorum of Kafka controllers. The controller nodes comprise a raft quorum to elect the active controller that manages the metadata partition. This log contains everything that used to be found ZooKeeper: topic, partition, ISRs, configuration data, etc. will all be located in this metadata partition.

Using the Raft algorithm controller nodes will elect the leader without the use of an external system like ZooKeeper. The leader, or active controller, will be the partition leader for the metadata partition and will handle all RPCs from the brokers.

Learn more about Kafka partitions >>

The diagram below is a logical representation of the new cluster environment implementation using KRaft:

Note in the diagram above there is no longer a double-sided arrow. This denotes another major difference in the two environments: Instead of the controller sending updates to the brokers, controllers fetch the metadata via a MetadataFetch API. In similar fashion to a regular fetch request, the broker will track the offset of the latest update it fetched, requesting only newer updates from the active controller persisting that metadata to disk for faster startup times.

In most cases, the broker will only need to request the deltas of the metadata log. However, in cases where no data exists on a broker or a broker is too far out of sync, a full metadata set can be shipped. A broker will periodically request metadata and this request will act as a heartbeat as well.

Previously, when a broker entered or exited a cluster, this was kept track of in ZooKeeper, but now the broker status will be registered directly with the active controller. In a post-ZooKeeper world, cluster membership and metadata updates are tightly coupled. Failure to receive metadata updates will result in eviction from the cluster.

ZooKeeper Deprecation and Removal

KRaft has been considered “production ready” since Kafka 3.3 and ZooKeeper was officially deprecated in Kafka 3.5. It will be removed completely in Kafka 4 and higher.

 

Running Kafka Without ZooKeeper

As organizations plan their migrations to KRaft environments, there are quite a few things to consider. In a KRaft-based cluster, Kafka nodes can be run in one of three different modes know as process.role. The process.role can be set to broker, controller, or combined. In a production cluster, it is recommended that the combined process.role should be avoided — in other words, having dedicated JVM resources assigned to brokers and controllers. So, as mentioned previously, doing away with ZooKeeper doesn’t necessarily mean doing away with the compute resources in production. Using the combined process.role in develop or staging environments is perfectly acceptable.

Since we originally published this blog, several upgrades and changes to the KRaft implementation have been completed and released. The list of caveats previously mentioned have largely been addressed, including:

• Configuring SCRAM users via the administrative API: With the completion and implementation of KIP-900 in Kafka 3.5.0 for inter-broker communications, the kafka-storage tool can be used as a mechanism to configure SCRAM.
• Supporting JBOD configurations with multiple storage directories: JBOD support was introduced in 3.7 as an early access feature. With the completion of KIP-858 and its implementation in 3.8, that is no longer the case.
• Modifying certain dynamic configurations on the standalone KRaft controller: In early releases of Kafka KRaft, some configuration items could not be updated dynamically, but as far as we are aware, these have mostly been fixed.  The “missing features” verbiage should be removed with 4.0 (see conversation here).
• Delegation tokens: KIP-900 also paved the way for “delegation token” support.  With the completion of KAFKA-15219 in 3.6, delegation tokens are now supported in KRaft mode.

KRaft Migration

Although a fully-fledged and supported upgrade path has been implemented and can be used to move clusters from Zookeeper mode to KRaft mode, in-place upgrades generally should be avoided. At OpenLogic, we typically recommend lift-and-shift style, blue-green deployments instead. However, given the complexity of some Kafka clusters, having an official migration path is very much a welcome tool in the tool belt.

While detailing the KRaft migration process would require an entire series of blog posts, you can find an overview of the process in the Kafka documentation here. Of particular interest, though, is the requirement to upgrade to Kafka 3.9.0. The metadata version cannot be upgraded during the migration, so inter.broker.protocol.version must be at 3.9 prior to the migration. So, even if your organization isn’t planning on migrating to KRaft anytime soon, it still makes sense to plan your upgrade to 3.9 sooner rather than later.

KRaft Mode FAQ

What benefits would my organization see, if any, from migrating to KRaft?

The most basic benefit for any organization is of course being able to stay up to date on your software versions. With ZooKeeper removal on the horizon, staying updated in ZK mode will eventually be impossible. Also, organizations will see a decrease in cluster complexity as Kafka will handle metadata natively instead utilizing third-party software.

Lastly, organizations operating at the upper levels of cluster size will see a considerable increase in reliability and service continuity in KRaft mode. While ZooKeeper is a reliable coordination service for a myriad of open source projects, whenever our customers with extremely large clusters (i.e. 30/40+ brokers with thousands of partitions) are encountering severe issues, it often winds up being a ZooKeeper issue.

If we migrate from ZooKeeper to KRaft, can we decommission our dedicated ZK hardware?

Most likely, no, at least not in production. Production KRaft controllers should be deployed in dedicated controller mode, so they will need dedicated compute just like ZooKeeper in production does. However, non-production clusters can run in mixed mode.

We have “N” number of ZooKeeper nodes; how many KRaft controller nodes should we use?

At the very minimum organizations should deploy at least 3 controller nodes in production. The system requirements for ZooKeeper and controller nodes are quite similar, though, so deploying the same number of controller nodes is a reasonable place to start. Ultimately, a thorough load and performance testing protocol should be followed to validate this.

If we are running Kafka via Strimzi Kubernetes operator, can we start using KRaft?

Yes! However, be aware that as of version 0.45.0, there some limitations with controller. Currently, Strimzi continues to use static controller quorums, which means there are some limitations on using dynamic controller quorums. More information can be found in the Strimzi documentation here.

Final Thoughts

For greenfield implementations, using KRaft should be a no-brainer, but for mature Kafka environments, migrating will be a complete rip and replace for your cluster, with all the complications that could follow. Creating a detailed migration plan, with blue/green deployment strategies, is crucial in such cases. And if your team is lacking in Kafka expertise, seeking out external support to guide your migration would also be a good idea.

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On June 30, 2024, the ten-year run of CentOS 7 came to an end, but more significantly, the date marked all versions of CentOS Linux reaching end of life. Now, more than six months later, many organizations are still planning their migrations to other community-supported Enterprise Linux distributions. They are exploring CentOS alternatives in the hopes avoiding the squeeze from Red Hat and having to pay for Extended Life Cycle Support, on top of RHEL licenses for each host that needs to be kept secure (which is usually all of them).

One popular migration path is CentOS to Rocky Linux. In other blogs, we’ve looked at why many have chosen Rocky Linux, but here, we’ll focus on the primary considerations for organizations that haven’t made the move yet, highlight potential risks, and explain how to evaluate the readiness of a given enterprise architecture to move from one major Enterprise Linux version (6, 7, and 8) to the latest version, Enterprise Linux 9. We’ll also provide a step-by-step walkthrough of a CentOS 7 to Rocky Linux 9 migration.

Step 1: Evaluate Whether Your Applications Are Ready for Rocky Linux Migration

How were you made aware that your team has out-of-compliance, End-of-Life CentOS hosts in their architecture in the first place? Perhaps your IT department sent you a email containing a list of these hosts, as the result of a scan. Or maybe you examined a list of AMIs in your Amazon Web Services (AWS) EC2 infrastructure, and came up with a list of affected hosts. Whatever way it happened, you have the list of hosts that need an upgrade, which means you have essentially completed the first step for migration: creating an inventory of hosts that need to make the move to Rocky Linux.

Ideally, you have a list of applications that are running on each of these hosts, and know the purpose of each. For example, “This one is a MariaDB server,” or “This runs Oracle 12c.” If you don’t, now is a good time to start building out a spreadsheet listing the CentOS hosts that need to be upgraded that includes the workload each is responsible for. Then create another inventory, for each host, of what software is installed. Find the owners of each machine, so that you can further examine what software is installed, perhaps in unconventional ways, that you might be missing.

Without knowing what software is on which machines, you won’t know the side effects of completing a major Enterprise Linux version upgrade. The main side effect of upgrading is new versions of the kernel, new versions of software in the yum / dnf repositories, and new versions of glibc and libstdc++. And this side effect can have some major unintended consequences.

Step 2: Decide Between Migrating in Place vs. Building a New System

The major consequences listed above are particularly important when deciding whether to do an in-place upgrade, or building a new system. You can either migrate a system to Rocky Linux in-place, or build a new system and migrate applications and data over. Each option has pros and cons, but let’s examine the three side effects of upgrading: new kernel, new versions of dnf-sourced applications, and new API and ABI versions.

To be clear, it’s not just the ABI contracts of glibc and libstdc++ that can stymie your upgrade plans. All bets are off regarding API and ABI compatibility between all libraries and packages between major versions of EL. Another risk of in-place upgrades is, in the end, the system may have a combination of packages from different EL versions; a few libraries from EL7 and an app from EL8 running on an EL9 box. Hybrid-version systems are incredibly difficult to troubleshoot, or even rebuild if not restoring from a full backup.

When you examine your inventory of hosts that need to be migrated off of CentOS, you need to determine if they’re bare-metal. Physical hosts are much more likely to have custom kernel modules or drivers built against the current kernel against source, source that’s often proprietary. Perhaps the host is connected to a tape library, has a PCI-E card like a graphics card, or has another peripheral that’s connected to an industrial application from a third-party manufacturer. For this reason, a hardware inventory is incredibly important: what peripherals or non-standard hardware are installed in the host?

Most open source drivers and kernel modules are going to seamlessly work from one major EL release to another, but add-ons that aren’t shipped with the server are more likely to need a driver to be recompiled against the new kernel in Rocky Linux. Make sure you can both obtain the source code for the driver and that it compiles against the new kernel. Otherwise you might successfully upgrade to Rocky Linux, but be stuck with an application that can’t reach a critical peripheral.

If you are on physical hardware as described above, migrating in place has the advantage that you don’t need more systems. This may be the easiest (and least expensive) route. You do have to make sure that connections are stable, and the machine will be available the whole time, because if the upgrade script is interrupted, the system may be left in an unrecoverable state. This would probably require a rescue disk and some manual work to get to a point where it is usable again. If you can’t take a system out of production to rebuild, then running the migration may be the best option, as the migration can be planned in a standard maintenance window.

However, if you are on a virtualized infrastructure, or have spare hardware, it would be safer to build a new system as you want it, then migrate the data and applications over and swap out the old system with the new. But even then, a software inventory is especially important due to the upgraded dnf-sourced software and upgraded glibc/libstdc++ libraries.

Step 3: Use a Software Inventory to Mitigate Risk

If your organization produces software with C++, it’s possible that you’re producing applications “dynamically linked” against system libraries. If you’re targeting CentOS 7, and you upgrade to Rocky Linux 9, the libraries you linked against are going to be upgraded, and the application you wrote might suddenly crash at runtime, even if it starts successfully after the upgrade.

This is because certain standard library methods might remain the same, allowing the application to start, but others might have been deprecated or changed, causing the application to crash when they’re no longer available. Because these safety checks are completed at compile-time, a runtime error might occur.

Even if your organization doesn’t maintain their own C++ applications, you might be installing applications from a third-party vendor that link against CentOS 7 libraries. If this vendor uses an external yum or dnf-based repository, there’s a good chance that the upgrade to Rocky might fail due to unresolved dependency issues. If the application is installed by downloading a .tar.gz, .sh, or .run file, and binaries are installed onto the host from that installer, there’s a strong possibility that the application might suffer similar crashes or incompatibilities from unexpected versions of C++ libraries, Python bindings, Lua versions, and the list goes on.

All of the above illustrates why it’s so important to have a software inventory of some kind. It could be as simple as a spreadsheet or a Software Bill of Materials (SBOM). Once you have that inventory, you can plan ahead and contact the vendors of third-party software, making sure they have an EL 8 or EL9 version of their software that can be upgraded to once your host becomes a Rocky Linux host.

As for the dnf-sourced packages, and considering the previously mentioned issues with version numbers changing for supporting libraries, moving from CentOS 7 to Rocky Linux 9 can include some major upgrades. For example, the upgrade from MariaDB 5.5, which was released in April 2012, to MariaDB 10.5.27, which shipped in May 2023. As you can imagine, there needs to be an end-to-end plan for this upgrade, and all of the hard-to-predict “ripples” it may cause.

What happens when you run into applications that can’t run on the new version of Rocky 8 or Rocky 9? One option would be containerizing old workloads in order to increase security, reducing the attack surface by running in an isolated container running on an up-to-date Rocky 9 host.

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Step 4: Determine Your CentOS to Rocky Linux Migration Path

There are a few considerations teams will need to make when migrating from CentOS to Rocky Linux. Depending on the CentOS version(s) being used, the upgrade path may require migrating to intermediary versions before arriving at the intended version. (E.g., CentOS 6 to CentOS 7, CentOS 7 to CentOS 8, CentOS 8 to Rocky Linux 8, then Rocky Linux 8 to Rocky Linux 9).

Approaching your upgrade with this step-wise approach is also useful for mitigating the risks described above. Catching incompatibilities early on in the upgrade process will be critical for informing leadership of how long this process is actually going to take.

Migrating CentOS 6 to Rocky Linux

Unfortunately, there is no direct migration path from CentOS 6 to Rocky Linux. Rocky Linux starts at 8, so hosts will have to be on CentOS 8 to migrate. As described above, there are too many changes between CentOS 6 and 7, much less 8, to migrate. Once you’ve migrated to CentOS 7 you can migrate from CentOS 7 to CentOS 8 then migrate to Rocky Linux 9, or migrate from CentOS 8 to Rocky Linux 8 then upgrade to Rocky Linux 9.

For CentOS 6 users there are two ways to do this.

1. Upgrade from CentOS 6, to 7, to 8, then migrate to Rocky Linux 8, then upgrade to Rocky Linux 9. This process can take a considerable amount of time, and can run into some additional hiccups due to the package changes between major versions.
2. Build a new machine and migrate your data over. The best case scenario with this approach is that all third party software needed in the new machine has a new version, and all your data can be upgraded safely. The worst case is that you’ll have software with no new version and will need to find alternatives or find a way to run the software anyway. This depends on the libraries being used in your CentOS 6 install. Luckily, containerization makes it easy to run older versions of software on newer systems, or even on completely different distributions entirely.

Migrating CentOS 7 to Rocky Linux

The migration path for CentOS 7 to Rocky Linux is similar to CentOS 6. However, migrating from CentOS 7 to Rocky Linux is a bit easier because CentOS 7 already uses systemd for service management, while CentOS 6 still uses legacy SysV init scripts.

There are a few other changes to keep an eye out for when moving from CentOS 7 to Rocky Linux, but, aside from the service management difference, the considerations are nearly identical to CentOS 6 migration.

Video: CentOS 7 Migration Recommendations

Migrating CentOS 8 to Rocky Linux

Migrating from CentOS 8 to Rocky Linux 8 is relatively painless, and avoids all of the risks described above. Since it is nearly identical, there are only a few changes, which makes this the least risky and easiest to validate step in the process. The repos are swapped out for Rocky Linux repos, and a few packages are replaced (mostly branding packages, for example, the package that provides all of the logos for CentOS).

Real-World Example: Upgrading CentOS 7 to Rocky Linux 9

1. Install the Latest Version of the ELevate Repository from the AlmaLinux Project

2. Install the LEAPP Package

Specifically, we are going to install the leapp-data-rocky package, which will help us move to Rocky Linux, as opposed to AlmaLinux:

3. Run the Pre-Upgrade Checks

The leapp-report.txt that is generated from the pre-upgrade command contains some canned resolutions to the errors it generated. Let’s try some of those answers!

There are plenty of other messages that were generated and put in the report, but only the ones we used above were necessary for the upgrade to move forward. If you have third-party repositories, there’s a possibility that upgraded versions of system dependencies might be present. You’ll have to manually remove or downgrade those packages to resolve these version inconsistencies.

In order for the upgrade to proceed, you can’t have any errors that will inhibit the upgrade. This will appear in the “Reports Summary” under “Inhibitors.” You may address some of the warnings that appear by examining the report and testing the suggestions they provide to see if you can resolve the warnings. Keep in mind only the inhibitors are required to proceed, though.

4. Run the Upgrade Process, Reboot, and Test

$ leapp upgrade

Now that you’ve booted into the new environment, you should see that you’re running on Rocky 8. Run through your standard QA tests to make sure the services the host is providing all work as expected. Continuing the upgrade to Rocky 9 isn’t likely to fix services that are broken at this stage, so conduct a thorough check before continuing the upgrade.

5. Upgrade to Rocky 9

The steps for continuing the upgrade to Rocky 9 are similar to the steps we took to upgrade to Rocky 8, starting with the ELevate repo:

$ yum install -y http://repo.almalinux.org/elevate/elevate-release-latest-el$(rpm –eval %rhel).noarch.rpm

Next, we can remove package exclusions that were added from the previous LEAPP upgrade:

$ yum config-manager –save –setopt exclude=”

You might run into a scenario like we did where we had to remove LEAPP with its dependencies, because because leapp-upgrade-el7toel8 was still installed, and it failed because it didn’t match the version. Then you can install the following:

$ yum install -y leapp-upgrade leapp-data-rocky

And then run the LEAPP preupgrade again: $ leapp preupgrade

The output of the report will be similar to the previous one. After examining the report, we had to run these steps:

Note: If root login is allowed, the report will fail. We resolved this by overriding our sshd_config:

Here was our last report before we ran the upgrade again:

Let’s give it a shot!

$ leapp upgrade

Again, even on a simple system, we can get blocking errors:

Looking at /var/log/leapp/leapp-report.txt, there are a number of warnings, including a conflict between rocky-logos 86.3-1.el8 and rocky-logos-90.15-2.el9:

file /usr/share/redhat-logos from install of rocky-logos-90.15-2.el9.x86_64 conflicts with file from package rocky-logos-86.3-1.el8.x86_64

To resolve this, we removed rocky-logos (which also removed rocky-backgrounds), and re-ran the LEAPP upgrade. Our next step was reboot, just as we did during the CentOS 7 to Rocky 8 upgrade, and select the grub entry ELevate-Upgrade-Initramfs again, and watch it go!

Upon rebooting, it was time to remove the excludes again:

$ yum config-manager –save –setopt exclude=”

Then we could remove orphaned packages, which cleans up the system and makes it more secure:

$ rpm -qa | grep -E ‘el8[.-]’ | xargs rpm -e

The same goes for the LEAPP packages, since they’re not needed anymore:

$ dnf remove $(rpm -qa|grep leapp)

Done! Now it’s time to run our E2E and integration tests. After thorough testing of this host, it will be ready to re-enter production as an upgraded system.

Final Thoughts

Organizations with a long list of hosts in their host inventory, or hosts with especially complex software inventories, may need assistance sorting out all of the complexities associated with a CentOS to Rocky Linux migration. Or they might need more time than they initially allotted for the project. Partnering with OpenLogic for CentOS long-term support or migration services can ease the burden considerably. Our Professional Services team can help you plan your migration or provide hands-on-keyboard support throughout the process. And after you have successfully migrated, our Enterprise Architects can offer Rocky Linux support up to 24-7.

“What’s the best Linux distro?”

A better question to ask: “Which Linux distro can meet my business’s needs now and as we scale?”

Now that CentOS Linux has reached end of life, the playing field has widened, with several viable alternatives. This blog gives an overview of the post-CentOS EOL Linux landscape, comparing the most popular Enterprise Linux distributions and highlighting key differentiators. As you read, keep in mind your own team’s bandwidth and expertise with managing Linux infrastructure as you’re evaluating factors like cost, stability, and security.

As those in the process of migrating off CentOS know all too well, longevity is important, too. Confidence in the project’s direction, the strength of the community, governance model (i.e. how much control a for-profit corporation has) — these are all considerations that could (and should) influence which open source Linux distro is the right fit for your organization.

Types of Open Source Linux Distributions

Linux distros are a combination of the open source Linux kernel and a suite of supporting software that facilitates the development and operation of applications. Open source communities make decisions about which packages to include based on the use cases they want to prioritize. A Linux distribution designed for desktop, for example, might include tools like media players and UI customizability features. Enterprise Linux distros, on the other hand, focus more on security, stability, and speed to optimize performance for mission-critical applications.

There are a few different ways to categorize open source Linux distros. You can bucket them according to who manages the project (a community or a commercial entity), the release model (rolling or fixed), or the upstream source (e.g. Fedora, Debian).

Community vs. Commercial

The difference between community vs. commercial open source Linux distributions is that community-backed Linux distributions are free to use and supported by a community made up of individual contributors. These volunteers dedicate time and expertise to maintain the project and commit to releasing security updates, bug fixes, and new versions.

Commercial Enterprise Linux distributions are sold by software vendors who build their product from open source components and packages and require a paid subscription. The distro itself is functionally identical to the community version, but users have access to technical support, and often some proprietary enterprise features/tooling.

Rolling Release vs. Fixed Release

Rolling release means that updates and new features are continuously and incrementally released instead of bundled into versions that are released on a fixed schedule. Frequent updates to the Linux kernel, libraries, utilities, or any package are released as soon as they are ready without waiting for a defined release date. Typically, rolling release Linux distros do not require users to perform large-scale version upgrades because of this “steady drip” of updates. Issues, bugs, and vulnerabilities can be identified and resolved more rapidly compared to fixed, or regular, release distros.

Rolling release distros appeal to those who prioritize having the latest software and features over stability. However, they require users to stay proactive in system maintenance and be prepared to address issues that arise due to the constant stream of updates. Rolling release models can often lead to conflicts between different software versions as no testing is done to validate that different software interoperates correctly; sometimes new features in a new package release can also lead to subtle behavior differences that cause application breakage. As such, many organizations prefer fixed release models for their business critical applications.

Upstream Source

There are distros derived from Fedora, RHEL (which itself comes from Fedora), Debian, SUSE, and more. Each ecosystem has strengths, and preference here might come down to what your team is accustomed to and other considerations (for instance, if you are already an Oracle customer, Oracle Linux might make more sense than if you are not).

Now let’s take a closer look at some of the distributions themselves, grouping them by their upstream source and starting with Fedora.

Note: Asterisks denote that the distribution is currently supported by OpenLogic. 

Fedora and RHEL-Based Linux Distros

Fedora*

Fedora is a popular, community-backed Linux distro known for its emphasis on new features and technologies, and open source collaboration. It aims to provide a platform for both desktop and server users, offering the latest software while maintaining a balance between innovation and stability. Fedora users appreciate staying on the forefront of technology, contributing to open source projects, and experimenting with the latest software innovations. Fedora typically releases two new versions a year, one in the spring and one in the fall.

CentOS Stream*

CentOS Stream is referred to as the “rolling preview” on which RHEL releases are based. It is the bridge between Fedora and RHEL, using the same source code Red Hat uses to produce the next version of RHEL. The current version is CentOS Stream 10 and precedes RHEL 10 (and downstream RHEL distros like Rocky Linux and AlmaLinux).

Picking CentOS Stream comes down to your preferences for your overall Linux ecosystem. Everything that you expect inside a RHEL/CentOS ecosystem, such as package manager and virtualization options, will still be available to you in Stream, and you’ll receive bug fixes and security patches on a faster schedule than on CentOS Linux. If you’re on the fence about the rolling release route and not sure your organization is ready, this CentOS Stream migration checklist is a good resource.

Red Hat Enterprise Linux (RHEL)

RHEL is a well-established commercial Enterprise Linux distro known for its stability, long-term support, and comprehensive ecosystem. It offers various editions tailored for different workloads and environments, such as servers, cloud, and container deployments. RHEL is built off of snapshots of CentOS Stream, freezing all software versions to those in the snapshot, and only applying security fixes going forward from that release version. This is what gives it stability and security.

Red Hat, now owned by IBM, provides support for RHEL customers, but the license cost and annual fees may be prohibitively expensive for some organizations. As with any commercial software, there is a greater risk of vendor lock-in as well.

CentOS Linux (Discontinued)*

Much to the community’s surprise (and dismay), CentOS 8 was prematurely sunsetted in 2021 just two years after its release and CentOS 7 reached end of life in 2024. Red Hat, who then controlled the project, announced the end of CentOS Linux as part of their decision to focus more on CentOS Stream. This led to the creation of new distros derived from the RHEL source code, most notably Rocky Linux and AlmaLinux, to replace CentOS Linux.

Migrating and decommissioning environments can take months (or even years), so CentOS long-term support is one option for businesses that need more time to evaluate other distros and transition their EOL CentOS deployments.

Rocky Linux*

Rocky Linux is a community-supported Linux distro created by one of the founders of CentOS and one of the most popular CentOS alternatives. Promising bug-for-bug compatibility with RHEL, Rocky Linux aims to provide a stable, reliable, and compatible platform for organizations and users who were previously relying on CentOS for their server infrastructure.

Related Blog >>Comparing Rocky Linux vs. RHEL

AlmaLinux*

Like Rocky Linux, AlmaLinux is a community-backed, open source Linux distro launched in response to the CentOS Linux project being discontinued. AlmaLinux is binary-compatible with RHEL, meaning that applications will run on AlmaLinux as seamlessly as in RHEL.

Oracle Linux*

Oracle Linux is packaged and distributed by Oracle, and is another binary-compatible rebuild of RHEL’s RPMs. Oracle Linux is tested and optimized to work well with Oracle’s other software offerings, making it a suitable choice for running Oracle databases and other application workloads. Some worry that eventually Oracle might start charging for Oracle Linux (like they did with OracleJDK in 2019), but as of now, it is free — and at a price point similar to RHEL, you can purchase SLA-backed commercial support.

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Debian-Based Linux Distributions

Debian Linux*

Debian is known for its commitment to open source principles, stability, and extensive package management system. It serves as the foundation for various other Linux distros such as Ubuntu and Linux Mint. Debian is widely used in both desktop and server environments. It is a popular choice for users seeking a reliable and customizable Linux distro for a wide range of applications and use cases, including embedded systems.

Debian Testing

Debian also has a testing branch, similar to a beta version, which is an intermediary stage between Debian’s unstable and stable branches. The testing branch is intended for users who want a balance between access to newer software and a relatively stable system. Debian Testing gets new features and fixes before the stable Debian release so there might be issues to troubleshoot in exchange for access to the latest and greatest features, some of which make their way into the stable Debian release.

Ubuntu Community Edition*

Often referred to as simply “Ubuntu,” this distro is widely used due to its user-friendly experience, robust software ecosystem, and active community support. It is a solid choice for both desktop and server Linux, and enterprise use. Like Debian, Ubuntu also uses the apt ecosystem for package management and many AI-related packages are included in the distro.

Ubuntu Pro

Ubuntu Pro is the commercialized version of Ubuntu known for its ease of use, regular updates, and compatibility with cloud environments. There are versions optimized for different environments, such as Ubuntu Desktop, Ubuntu Server, Ubuntu for IoT, and Ubuntu Cloud. Ubuntu attracts front-end developers with easy-to-use features and a slew of programming resources, including AI libraries.

Linux Mint

Linux Mint strives to provide a stable, user-friendly experience for both Linux newcomers and experienced users. It is based on Ubuntu and Debian, building upon their foundations while adding additional features and design elements. Linux Mint emphasizes convenience and provides a traditional desktop experience with a lot of customization options. It also was designed to help Windows users seamlessly transition to a Linux OS.

SUSE Distributions

OpenSUSE Leap*

OpenSUSE Leap is a community-driven distro that combines the stability of a fixed release model with the availability of up-to-date software packages. It provides a reliable and user-friendly operating system for both desktop and server environments. OpenSUSE is generally considered to be stable for production use, and those familiar with the SLES, SUSE, and Slackware ecosystem will feel comfortable in this environment. OpenSUSE focuses on deployment simplicity, user-friendly toolchain, and cloud-readiness.

OpenSUSE Tumbleweed*

Tumbleweed is the OpenSUSE community’s rolling release distro. Just as in CentOS Stream, bug fixes and security patches come earlier than in OpenSUSE Leap, the regular release distro, but there also could be some features that are not quite ready for primetime. Tumbleweed supports a wide range of desktop environments, software libraries, and tools.

SUSE Linux Enterprise Server (SLES)

SLES is the commercial counterpart to the OpenSUSE Linux distros and is backed by SUSE, a German-based multinational enterprise. It is an enterprise-focused distribution with a strong emphasis on reliability, scalability, and high-performance computing. It offers features like Systemd, Btrfs, and containers support, making it suitable for various server and virtual environments.

Other Open Source Linux Distributions

Arch Linux

Arch Linux is a rolling, lightweight Linux distro that is highly customizable and emphasizes simplicity, minimalism, and a DIY approach. It is a better fit for experienced Linux users who want to build a tailored and efficient OS environment according to their specific needs. Its rolling release model provides continuous updates to the latest software packages and features without the need for major version upgrades. Arch Linux is popular among developers and Linux enthusiasts (aka “power users”) who enjoy experimenting with and fine-tuning their Linux system.

Alpine Linux

Alpine Linux is a security-oriented lightweight Linux distro designed for resource efficiency and containerization. It is known for its small footprint, speed, and focus on security measures. Alpine Linux is often used in scenarios where size and security are critical, such as in containers, IoT devices, and embedded systems. Alpine Linux is particularly suitable for scenarios where fast boot times, small memory usage, and strong security are required.

Amazon Linux

Amazon Linux is AWS’s Linux distro intended for use in Amazon Elastic Compute Cloud environments (EC2). It is offered as pre-configured Amazon Machines Images (AMI) ready to use in AWS. Originally built from RHEL, the distro is now derived from CentOS Stream, and the source code is publicly available and distributed under open source licenses.

Final Thoughts

Hopefully it is clear by now that choosing the best Linux distro for your organization will likely take some time and research. Considering what each offering can help your business achieve, and where you might find friction in implementation is key to succeeding with your next open source Linux distro. Make sure you think about intended use cases, the skills required, and learning curve. Tooling (such as package management) is important to evaluate, along with ecosystem, compatibility, and vendor lock-in risk.

One way to avoid vendor lock-in but still get the security and support you need is to partner with a third party like OpenLogic. Our Enterprise Linux support is guaranteed by SLAs and every ticket is handled by an Enterprise Architect with at least 15 years of Linux experience. We also offer migration services – from consulting to executing the migration itself.

Editor’s Note: This blog was originally published in January 2021. It was updated in February 2025 to reflect changes in the open source Enterprise Linux landscape.

Data Storage and Processing

The primary purpose of Big Data storage is to successfully store vast amounts of data for future analysis and use. A scalable architecture that allows businesses to collect, manage, and analyze immense sets of data in real-time is essential. 

Data Lakes

Data Warehouses

Data Pipelines

Related Technologies

Data Mining

Data mining is defined as the process of filtering, sorting, and classifying data from large datasets to reveal patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. 

Association Rule

Classification

Clustering

Regression

Sequence & Path Analysis

Neural Networks

Prediction

Related Technologies

Data Visualization

Data visualization is the graphical representation of information and data. With visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Related Technologies

Final Thoughts

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Why Companies Need OSS Support

The Top 5 Reasons Companies Choose OpenLogic for OSS Support

For more than 20 years, OpenLogic has offered expert OSS technical support and professional services (i.e. consulting, migrations, training) to organizations around the world. Below are insights from customers sharing what made them pick OpenLogic as their OSS partner. 

1. One Vendor Who Can Support All the OSS in Your Stack

2. Consistent, Direct Support From Experienced Enterprise Architects

3. Meet Compliance Requirements With SLA-Backed Support

4. Expertise Integrating Open Source Packages Into Full Stack Deployments

5. Unbiased Guidance Regardless of Infrastructure or Environment

Keeping track of software dependencies is not an easy task and only becomes more difficult as companies scale. In this blog, we explore the types of dependencies and complications they can cause, as well as available tools and best practices organizations can adopt to improve their open source inventory management.

Understanding Software Dependencies

Software dependency management is a hot topic and an ongoing area for learning and process improvements. They are the byproduct of code collaboration and sharing, and all of us who consume and/or contribute to OSS are potential victims of the consequences if dependencies aren’t properly managed. And while not unique to open source software, the rapid proliferation of open source technologies has made tracking software dependencies more complex.

There are two main categories of software dependencies:

• Direct dependencies: This refers to frameworks, libraries, modules, and other software components that an application deliberately and “directly” references to address a solved problem.
• Transitive dependencies: This refers to the cascading list of those independent pieces of software that the direct dependencies in turn include to function properly.

Beyond that, there are some distinctions within those two main categories that are good to be aware of before defining a dependency management strategy:

• Internal vs. External: Some dependencies may be owned and controlled internally by a development team, though typically the vast majority are created and maintained externally.
• Open vs. Closed: Referenced dependencies may be open source allowing development team investigation and ownership by proxy, or they might be binary-only licensed from a vendor where changes are managed through contractual terms.
• Idle vs. Engaged: As the application source evolves, needs change, rendering some dependencies irrelevant. However, they are not always removed from the dependency chain. As a result, some dependencies are actively engaged and used, whereas others are no longer used and remain bundled but idle.

A software inspection methodology that includes inventorying dependencies and managing lifecycles is essential to system security and sustainability. An up-to-date software inventory is necessary for identifying vulnerable or end-of-life components, and identification is the first step in remediating issues and mitigating risks.

The Challenges of Dependency Management

Today there is an ever-increasing demand for both speed and innovation with regards to software development, and that is both the catalyst for, and the result of, open source software. This demand has also produced software delivery concepts like microservices and container orchestration that require vast amounts of integration points – all of which contribute to the chain of software dependencies. This has ushered in a host of software maintenance problems that require dependency management solutions.

The main challenges that arise are due to the pace of change. It is increasingly more difficult for organizations to keep up with evolving software, as well as the companies, communities, and licensing bodies that maintain and govern them. Some examples:

• Version conflicts: When multiple dependencies within the same application require different versions of a shared library.
• Compatibility issues: When updating a package can introduce breaking changes that require modifications to your application to maintain existing functionality.
• Security vulnerabilities: When a downstream dependency has a known security defect that either needs to be addressed by your application or requires an update to the dependency to remediate it.
• End-of-Life problems: When the referenced software package is no longer maintained by the vendor or community, which can result in security defects that are not remediated and leave your application vulnerable to attacks.
• License compliance: When the application uses another software component in a way that is not allowed by the software license. This can sometimes happen as the result of a license change as versions of the dependency are upgraded.
• Idle bloat: When an application has a growing number of unreferenced dependencies that increase the size, complexity, and liability without adding value.

Few developers are privy to all dependency management best practices, and most teams are not equipped with the tooling necessary to mount a proactive approach to avoid dependency problems. Gone are the days when a development team would settle on a single programming language that allowed them to use a particular package manager (e.g. python:pip, java:maven, javascript:npm, rpm:yum) to list the dependency tree, checklists to track the inventory, and unit tests to validate upgrades. Professionalizing a software development practice now requires modern systems for tackling software dependency management at scale.

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How to Track Software Dependencies and Manage Your Open Source Inventory

Unfortunately as of this writing, there is no silver bullet in this space. In fact, there is not even a best-in- class solution that has emerged. The good news is, there are software organizations and communities that recognize the problem, and are developing strong solutions to address pieces of this puzzle. Gluing them together can produce an effective system, which is the best path forward for now.

Software Dependency Management Tools

There are a few cornerstone tools that lay the foundation for a modern software dependency management system:

• A central code repository that supports revision control and release versioning (e.g. Git, Github, Gitlab, Helix Core). This is the foundation for dependency discovery, and it can also save and manage lock files that tie an application to a specific version of a dependency.
• A package manager for each programming language or platform (e.g. python:pip, java:maven, javascript:npm, rpm:yum). These tools will handle the interactions (push, pull, install, update, list, etc.) with a dependency repository.
• A Software Bill of Materials (SBOM) generator (Syft, SBOM Tool, Tern, CycloneDX). This will produce an attributed inventory of all the software components in your applications (including supplier name, component name, component author, component version, dependency relationship, governing license(s), etc.).
• A vulnerability scanner that supports scheduled detection scans and notification schemes (e.g. Trivy, Grype). This tool will schedule automatic scans that identify security issues and provide detailed reports (i.e. risk prioritization, remediation guidance) that help assess the impact to all direct and transient dependencies referenced by your application.

6 Dependency Management Best Practices

The tools above should be augmented by some best practices that can be implemented and enforced through internal policies, processes, and procedures. These six best practices are a good place to start:

1. Create a central artifact repository to capture the software inventory with key attributes, notes, and links to additional details in related systems (i.e roadmapping, issue tracking systems, risk management, contracts).
2. Define a clear dependency policy that lists acceptable and unacceptable sources and specific approved lists of software components, along with guidelines for gaining approval for components that fill new needs.
3. Establish update and upgrade policies that describe the tooling used to scan for dependency vulnerabilities and lifecycle attributes with guidance on how to prioritize, schedule, and apply/defer the scanner’s findings.
4. Develop a training curriculum to educate developers and others in the organization on the need for ongoing diligence around dependency management and the topics, tools, and techniques required to deliver and maintain a healthy application.
5. Adopt a versioning scheme (i.e. semantic versioning) that allows the organization to track the alignment of dependencies to a particular version of an internal application.
6. Require formal code reviews and testing that includes a dependency review geared toward heading off the common challenges identified above (e.g. version conflicts – idle bloat).

Final Thoughts

Software has become progressively more complex and the need for speed has driven more code-sharing and reuse. Developers have to rely on available packages to handle solved problems, so they can focus on new challenges that advance their particular mission. And unfortunately, sometimes tracking all the dependencies in those packages gets lost in the DevOps shuffle. Hopefully, this blog offers some actionable steps to make your approach to dependency and open source inventory management a little more sophisticated.

Apache Spark vs. Hadoop isn’t the 1:1 comparison that many seem to think it is. While they are both involved in processing and analyzing Big Data, Spark and Hadoop are actually used for different purposes. Depending on your Big Data strategy, it might make sense to use one over the other, or use them together.

In this blog, our expert breaks down the primary differences between Spark vs. Hadoop, considering factors like speed and scalability, and the ideal use cases for each.

What Is Apache Spark?

Apache Spark was developed in 2009 and then open sourced in 2010. It is now covered under the Apache License 2.0. Its foundational concept is a read-only set of data distributed over a cluster of machines, which is called a resilient distributed dataset (RDD).

RDDs were developed due to limitations in MapReduce computing, which read data from disk by reducing the results into a map. RDDs work faster on a working set of data which is stored in memory which is ideal for real-time processing and analytics. When Spark processes data, the least-recent data is evicted from RAM to keep the memory footprint manageable since disk access can be expensive.

What Is Apache Hadoop?

Hadoop is a data-processing technology that uses a network of computers to solve large data computation via the MapReduce programming model.

Compared to Spark, Hadoop is a slightly older technology. Hadoop is also fault tolerant. It knows hardware failures can and will happen and adjusts accordingly. Hadoop splits the data across the cluster and each node in the cluster processes the data in parallel very similar to divide-and-conquer problem solving.

For managing and provisioning Hadoop clusters, the top two orchestration tools are Apache Ambari and Cloudera Manager. Most comparisons of Ambari vs. Cloudera Manager come down to the pros and cons of using open source or proprietary software.

Apache Spark vs. Hadoop at a Glance

The main difference between Apache Spark vs. Hadoop is that Spark is a real-time data analyzer, whereas Hadoop is a processing engine for very large data sets that do not fit in memory.

Hadoop can handle batching of sizable data proficiently, whereas Spark processes data in real-time such as streaming feeds from Facebook and Twitter/X. Spark has an interactive mode allowing the user more control during job runs. Spark is the faster option for ingesting real-time data, including unstructured data streams.

Hadoop is optimal for running analytics using SQL because of Hive, a data warehouse system that is built on top of Hadoop. Hive integrates with Hadoop by providing an SQL-like interface to query structured and unstructured data across a Hadoop cluster by abstracting away the complexity that would otherwise be required to write a Hadoop job to query the same dataset. Spark also has a similar interface, Spark SQL, which is part of the distribution and does not have to be added later.

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Spark vs. Hadoop: Key Differences

In this section, let’s compare the two technologies in a little more depth.

Ecosystem

The core computation engines of Hadoop and Spark differ in the way they process data. Hadoop uses a MapReduce paradigm that has a map phase to filter and sort data and a reduce phase for aggregating and summarizing data. MapReduce is disk-based, whereas Spark uses in-memory processing of Resilient Distributed Datasets (RDDs), which is great for iterative algorithms such as machine learning and graph processing.

Hadoop comes with its own distributed storage system, the Hadoop Distributed File System (HDFS), which is designed for storing large files across a cluster of machines. Spark can use Hadoop’s HDFS as its primary storage system, but it also supports other storage systems like S3, Azure Blob Storage, Google Cloud Storage, Cassandra, and HBase.

Hadoop and Spark include various data processing APIs for different use cases. Spark Core provides functionality for Spark jobs like task scheduling, fault tolerance, and memory management. Spark SQL allows SQL-like queries on large datasets and integrates well with structured data. It supports querying both structured and semi-structured data. The Spark Streaming component provides real-time stream processing by dividing data streams into small batches. MLlib and GraphX are libraries for machine learning algorithms and graph processing, respectively, that run on Spark.

Hadoop includes MapReduce, which is the core API for data processing in Hadoop.  The following tools can be added to Hadoop for data processing:

• Apache Hive is a data warehouse system built on top of Hadoop for querying and managing large datasets using a SQL-like language.

• Apache HBase is a distributed NoSQL database that runs on top of HDFS and is used for real-time access to large datasets.

• Apache Pig is a platform for analyzing large datasets that uses a scripting language (Pig Latin) to express data transformations.

For cluster management, YARN (Yet Another Resource Manager) is the most common approach to run Spark applications to run transparently in tandem with Hadoop jobs in the same cluster which provides resource isolation, scalability, and centralized management.

Spark does have a few more cluster management configurations than Hadoop.  Apache Mesos is a distributed systems kernel that can run Spark, and Spark also has native support for Kubernetes, which can be used for containerized deployment and scaling capabilities in Spark clusters.

For fault tolerance, Hadoop has data block replication that ensures data accessibility if a node fails, and Spark uses RDDs to reconstruct data in the event of failure.

Real-time processing and machine learning are both included with Spark. Spark Streaming natively supports real-time data processing with low latency, but Hadoop requires tools like Apache Storm or Apache Flink to accomplish this task. MLLib is Spark’s machine learning library, and Apache Mahout can be used with Hadoop for machine learning.

Features

Hadoop has its own distributed file system, cluster manager, and data processing. In addition, it provides resource allocation and job scheduling as well as fault tolerance, flexibility, and ease of use.

Spark includes libraries for performing sophisticated analytics related to machine learning, AI, and a graphing engine. The scheduling implementation between Hadoop and Spark also differs. Spark provides a graphical view of where a job is currently running, has a more intuitive job scheduler, and includes a history server, which is a web interface to view job runs.

Performance and Cost Comparison

Hadoop accesses the disk frequently when processing data with MapReduce, which can yield a slower job run. In fact, Spark has been benchmarked to be up to 100 times faster than Hadoop for certain workloads.

However, because Spark does not access to disk as much, it relies on data being stored in memory. Consequently, this makes Spark more expensive due to memory requirements. Another factor that makes Hadoop more cost-effective is its scalability; Hadoop mixes nodes of varying specifications (e.g. CPU, RAM, and disk) to process a data set. Cheaper commodity hardware can be used with Hadoop.

Other Considerations

Hadoop requires additional tools for Machine Learning and streaming which come included in Spark. Hadoop can also be very complex to use with its low-level APIs, while Spark abstracts away these details using high-level operators. Spark is generally considered to be more developer-friendly and easy to use.

Spark Use Cases

Spark is great for processing real-time, unstructured data from various sources such as IoT, sensors, or financial systems and using that for analytics. The analytics can be used to target groups for campaigns or machine learning. Spark has support for multiple languages like Java, Python, Scala, and R, which is helpful if a team already has experience in these languages.

Hadoop Use Cases

Hadoop is great for parallel processing of diverse sets of large amounts of data. There is no limit to the type and amount of data that can be stored in a Hadoop cluster. Additional data nodes can be added to address this requirement. It also integrates well with analytic tools like Apache Mahout, R, Python, MongoDB, HBase, and Pentaho.

It’s also worth noting that Hadoop is the foundation of Cloudera’s data platform, but organizations that want to go 100% open source with their Big Data management and have a little more control over where they host their data should consider the Hadoop Service Bundle as an alternative.

Using Hadoop and Spark Together

Using Hadoop and Spark together is a great way to build a powerful, flexible big data architecture. Typical use cases are large-scale ETL pipelines, data lakes and analytics, and machine learning. Hadoop’s scalable storage via HDFS can be used for storing large datasets and Spark can perform distributed data processing and analytics. Hadoop jobs can be used for large and long-running batch processes, and Spark can read data from HDFS and perform complex transformations, machine learning, or interactive SQL queries. Spark jobs can run on top of a Hadoop cluster using Hadoop YARN as the resource manager. This leverages both Hadoop’s storage and Spark’s faster processing, combining the strengths of both technologies.

Final Thoughts

Organizations today have more data at their disposal than ever before, and both Hadoop and Spark have a solid future in the realm of open source Big Data infrastructure. Spark has a vibrant and active community including 2,000 developers from thousands of companies which include 80% of the Fortune 500.

For those thinking that Spark will replace Hadoop, it won’t. In fact, Hadoop adoption is increasing, especially in banking, entertainment, communication, healthcare, education, and government. It’s clear that there’s enough room for both to thrive, and plenty of use cases to go around for both of these open source technologies.

Editor’s Note: This blog was originally published in 2021 and was updated and expanded in 2025. 

It’s a new year, which is a good time to reflect on what changed in the never-boring OSS world over the past 12 months — and what 2025 might bring. Read on to see what I expect we’ll be hearing and reading about this year in terms of open source trends.

Demand for More Data Sovereignty

More and more organizations are streaming and processing large data sets in realtime, for reasons ranging from observability into manufacturing processes and sentiment analysis of social media, to routing and processing financial transactions and training Large Language Models for AI applications.

Big Data technologies are complex, often requiring both specialized IT operations teams as well as infrastructure architects. As a result, many companies have turned to managed solutions in order to offload this work so their own teams can focus on the data and data analysis itself. However, many of these managed solutions have started adding non-optional features, requiring public cloud deployment, and dramatically increasing their pricing structure, often without transparency to their customers. Additionally, customers are running into compliance issues, as new regulatory requirements mandating how and where data is processed and stored are sometimes incompatible with these platforms.

Since many of these solutions are based on existing OSS technologies such as Hadoop, Kafka, and others, we expect to see companies rethinking their Big Data strategy, looking for ways to achieve data sovereignty by bringing their Big Data solutions in-house with open source software, and partnering with commercial support vendors as needed to aid in architecture and management.

Related >> Is It Time to Open Source Your Big Data Management? 

The Search for the Next CentOS Continues

On June 30, 2024, we saw a milestone in the Enterprise Linux ecosystem as CentOS 7 reached end of life. While a number of commercial offerings emerged to allow CentOS users to postpone their migrations, these are short-term solutions, and eventually companies will need to migrate to new distributions.

As CentOS was itself a 1-to-1 replacement for Red Hat Enterprise Linux (RHEL), this of course remains an option. However, this ignores one of the main reasons for using CentOS: the fact that you could use it without support contracts, or contract with third parties for support, often at steep discounts over Red Hat.

Several CentOS alternatives have emerged in the past few years, including AlmaLinux and Rocky Linux, providing essentially the same 1:1 OSS counterpart to RHEL that CentOS provided. Like CentOS, these distros are community-supported, and both are relatively new, with an unproven track record of support that makes some enterprise organizations nervous.

Additionally, many businesses have become increasingly security-minded in the last few years, due to a variety of CVE announcements against OSS software as well as supply chain attacks. A freely available Linux distribution is often not enough for these companies; they also need a secure baseline image to start from in order to streamline the security measures they need to take to protect their software. While commercial solutions such as RHEL, Oracle Linux, and SUSE Linux provide these, they come at substantial cost.

All of which is to say, there is still no clear victor in the so-called “Linux Wars” but as more companies migrate off CentOS in 2025, we’ll probably have a better sense of whether security or cost-effectiveness is the bigger driver based on where they end up.

Related >>How to Find the Best Linux Distro For Your Organization

Open Source AI Enters the Next Phase

AI has become the technology du jour, replacing previously trending topics such as the metaverse and cryptography. Technically speaking, most of the technology around AI today is around Large Language Models (LLMs) and Generative AI, which use statistical models in order to determine what to do next, whether that’s completing a conversational prompt, splicing together images, or other use cases.

Generative AI models require large amounts of training, with large amounts of data — which means that it falls under the umbrella of Big Data when it comes to open source. The need to keep these processes and technologies secure and performant is paramount — and just like with Big Data, the amount of expertise is spread thin.

AI is a hugely competitive market and that’s not going to change in 2025. There are a variety of toolchains already available for training LLMs and other models within Big Data pipelines, with tools such as Apache Spark, Apache Kafka, and Apache Cassandra providing key functionality used to train these models. I anticipate seeing more companies developing bespoke LLMs that directly support the products they produce, and they will use open source toolchains to do this.

Related >>Open Source and AI: Using Cassandra, Kafka, and Spark for AI Use Cases

Lessons From the XZ Utils Backdoor

In 2024, the security world was rocked by the discovery of a malicious backdoor in the xz utility, and attention was turned to staving off future supply chain attacks.

Supply chain attacks? But isn’t xz an open source utility?

In this particular case, an individual had used social engineering to very gradually, over multiple years, take over maintenance of the open source project producing xz. Once they had, they slipstreamed in the backdoor in a release they signed.

While many tried to decry this incident as evidence that open source software is inherently insecure (as this sort of social engineering is always a possibility), there’s another side to the coin: it was an open source packager performing standard benchmarking on a development release of an operating system who uncovered the issue. As the adage goes, many eyeballs make all bugs shallow.

One side effect of this attack was renewed interest in Software Bills of Materials (SBOMs). Organizations that are able to produce an SBOM for their software have a record of what they have installed, including the specific versions, as well as what licenses apply. This provides the ability to audit your software — or your vendor’s software — for known security vulnerabilities, and to react to them more quickly. Many organizations are forming DevSecOps teams to manage building, maintaining, and validating SBOMs against vulnerability lists as part of ongoing security in-depth efforts.

Even better, the OSS community is stepping up to build tooling for producing SBOMs into their development chains and utilities. The Node.js community has several projects that will produce SBOMs from application manifests; PHP’s Composer project added these capabilities; Java’s Maven and Gradle each have plugins to generate SBOMs.

Security is and will continue to be a top concern for companies using open source software, and in 2024, we saw proof that the ecosystem is helping protect them. Whether or not we will have another zero-day attack in 2025 remains to be seen, but companies are recognizing the benefit of being more proactive by embedding security best practices into their development and operations workflows and managing OSS inventory with the assistance of tools like SBOMs.

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