It’s no secret that data collection has become an integral part of our everyday lives; we leave a trail of data everywhere we go, online and in person. Companies that collect and store huge volumes of data, otherwise known as Big Data, need to be strategic about how that data is handled at every step. With a better understanding of Big Data management and its role in strategic planning, organizations can streamline their operations and leverage their data analytics to optimize business outcomes. 

In this blog, our expert discusses some of the components of Big Data management strategy and explores the key decisions enterprises must make to find long-term success in the Big Data space. 

Why Strategic Big Data Management Matters

When Big Data technologies are effectively incorporated into an organization’s strategic planning, leaders can make data-driven decisions with a greater sense of confidence. In fact, there are numerous ways in which Big Data and business intelligence can go hand in hand.

One example of this is strategic pricing. With the insights gained from using data analysis techniques, it is possible to optimize pricing on products and services in a way that maximizes profits. This type of strategizing can be especially effective when Big Data solutions look closely at metrics such as competitor pricing, market demand trends, and customer buying habits or customer data analysis.

Big Data can play a key role in product development. Through the analysis of industry trends and customer behavior, businesses can determine exactly what consumers are looking for in a particular product or service. They can also narrow down pain points that may inhibit customers from purchasing, make changes to alleviate them, and put out better products as a result.

Understanding Big Data Management

Big Data refers to the enormous amounts of data that is collected in both structured and unstructured ways. The sheer size and amount of this data makes it impossible to process and analyze using “traditional” methods (i.e. databases). 

Instead, more advanced solutions and tools are required to handle the three Vs of Big Data: Data containing great variety, coming in increasing volumes, at high velocity. This data typically comes from public sources like websites, social media, the cloud, mobile apps, sensors, and other devices. Businesses access this data to see consumer details like purchase history and search history, to better understand likes, interests, and so on. 

Big Data analytics uses analytic techniques to examine data and uncover hidden patterns, correlations, market trends, and consumer preferences. These analytics help organizations make informed business decisions that lead to efficient operations, happy consumers, and increased profits.

Developing a Big Data Management Strategy

If you are planning to implement a Big Data platform, it’s important to first assess a few things that will be key to your Big Data management strategy.

Determine Your Specific Business Needs

The first step is determining what kind of data you’re looking to collect and analyze. 

• Are you looking to track customer behavior on your website?
• Analyze social media sentiment?
• Understand your supply chain better? 

It’s important to have a clear understanding of what you want to achieve before moving forward with a Big Data solution.

Consider the Scale of Your Data

The sheer amount of your data will play a big role in determining the right Big Data platform for your organization. Some questions to ask include:

• Will you need to store and process large amounts of data, or will a smaller solution be sufficient?
• Do you have a lot of streaming data and data in motion? 

If you’re dealing with large amounts of data, you’ll need a platform that can handle the storage and processing demands. 

Hadoop and Spark are popular options for large-scale data processing. However, if your data needs are more modest, a smaller solution may be more appropriate.

Assess Your Current Infrastructure

Before implementing a Big Data platform, it’s important to take a look at your current infrastructure. For example, do you have the necessary hardware and software in place to support a Big Data platform? Are there any limitations or constraints that need to be taken into account? What type of legacy systems are you using and what are their constraints?

It’s much easier to address these issues upfront before beginning the implementation process. It’s also important to evaluate the different options and choose the one that best fits your business needs both now and in the future.

Implementing a Big Data platform requires a high level of technical expertise. It’s important to assess your in-house technical capabilities before putting a solution in place.

If you don’t have the necessary skills and resources, you may need to consider bringing in outside help, outsourcing the implementation process, or hiring for the skill sets necessary.

Big Data Hosting Considerations

Where to host Big Data is the subject of ongoing debate. In this section, we’ll dive into the factors that IT leaders should weigh as they determine whether to host their Big Data infrastructure on-premises (“on-prem”) vs. in the cloud.

Keeping Big Data infrastructure on-prem has historically been a comfortable option for teams that need to support Big Data applications. However, businesses should consider both the benefits and drawbacks of this scenario. 

Benefits of On-Prem

• More Control: On-premises gives IT teams more control over their physical hardware infrastructure, enabling them to choose the hardware they prefer and to customize the configurations of that hardware and software to meet unique requirements or achieve specific business goals.
• Greater Security: By owning and operating their own dedicated servers, IT teams can apply their own security protocols to protect sensitive data for better peace of mind.
• Better Performance: The localization of hosting on-premises often reduces latency that can happen with cloud services, which improves data processing speeds and response times.
• Lower Long-Term Costs: While on-premises is a more costly option to buy and build upfront, it has better long-term value as a business scales up and uses the full resources of this investment.
• More  Uptime: Many IT teams prefer to be able to monitor and manage their server operations directly so they can resolve issues quickly, resulting in less downtime. 

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Drawbacks of On-Prem

• Higher Upfront Costs: As noted above, on-prem can be cost-effective at a larger scale or in the long-run, but the initial cost to buy and build the infrastructure can be restrictive to businesses that do not have budget to invest at the outset of their services.
• Staffing Constraints: To deploy an effective on-premises solution, an IT team that is qualified to both build and manage the infrastructure is necessary. If a business has critical services, this may require payroll for 24/7 staffing and the on-going expense of training and certifications to maintain the proper IT team skills.
• Data Center Challenges: On-premises also requires an adequate location to host the infrastructure. The common practice of racking up servers in ordinary closet spaces brings significant risks to security and reliability, not to mention adherence to proper safety guidelines or compliance requirements. Additionally, if the location uses conventional energy, the cost to operate power-hungry high-availability hardware can be significant.
• Longer Time to Deploy: Even with the right skills and resources, an on-premises solution can take weeks or months to actually build and spin up for production.
• Limited Scalability: On-premises gives IT teams the ability to quickly scale within their existing hardware resources. But when capacity begins to run out, they will need to procure and install additional infrastructure resources, which is not always easy, quick, or inexpensive.

As per the cloud options, the most conventional approach is for IT teams to partner with vendors that offer a broad portfolio of services to support Big Data applications, which alleviates the burdens of hardware ownership and management. 

While a popular decision, businesses again would be wise to consider both the pros and cons of public cloud-based Big Data platforms.

Pros of Public Cloud

• Rapid Deployment: Public clouds allow businesses to purchase and deploy their hosting infrastructure quickly. Self-service portals also enable rapid deployment of infrastructure resources on-demand.
• Easy Scalability: Public clouds offer nearly unlimited scalability, on-demand. Without any dependency on physical hardware, businesses can spin storage and other resources up (or down) as needed without any upfront capital expenditures (CapEx) or delays in time to build.
• OpEx Focused: Public clouds charge users for the cloud services they use. It is a pure operating expense (OpEx). As a result, public cloud OpEx costs may be higher than the OpEx costs of an on-prem or private cloud environment. However, as discussed previously, public clouds do not require the traditionally upfront CapEx costs of building that on-prem or private cloud environment.
• Flexible Pricing Models: Public clouds also give businesses the ability to use clouds as much or little as they like, including pay-as-you-go options or committed term agreements for higher discounts.

Cons of Public Cloud 

• More Security Risks: The popularity of public cloud platforms has enabled a wide variety of available security applications and service providers. Nevertheless, public clouds are still shared environments.As increasing processes are requested at faster speeds, data can fall outside of standard controls. This can create unmanaged and ungoverned “shadow” data that creates security risks and potential compliance liabilities.
• Less Control: In a shared environment, IT teams have limited to no access to modify and/or customize the underlying cloud infrastructure. This forces IT teams to use general cloud bundles to support unique needs. To get the resources they do need, IT teams wind up paying for bundles that include resources they do not need, leading to cloud waste and unnecessary expenses.
• Uptime and Reliability: For Big Data to yield useful insights, public clouds need to operate online uninterrupted. Yet it is not uncommon for public clouds to experience significant outages.
• Long-Term Costs: Public clouds are a good option for new business start-ups or services that require limited cloud resources. But as businesses scale up to meet demand, public clouds often become a more expensive option than on-prem or private cloud options. And, because of the complexity of public cloud billing, it can be very difficult for businesses to understand, manage, and predict their data management costs.

Overall, decisions on how and where to implement a comprehensive Big Data solution should be made with a long-term perspective that accounts for costs, resources alignment, and scalability goals.

Big Data Management Considerations

On the surface, it seems ideal to keep all your business functions in-house, including the ones related to Big Data implementations. In reality, however, it is not always an option, especially for companies that are scaling quickly, but lack the expertise and skills to manage projects of the complexity and depth that Big Data practices demand.

In this section, we will explore what organizations stand to lose or gain by outsourcing expertise when it comes to their Big Data management and maintenance.

Benefits of Outsourcing Big Data Management

• Access to Advanced Skills and Technologies: Outsourcing the management of Big Data implementations allows businesses to tap into a pool of specialized skills and cutting-edge technologies without the overhead of developing these capabilities in-house. As technology rapidly evolves, third party partners must stay ahead by investing in the latest tools and training for their teams. So they absorb that cost, instead of their customers.
• Reducing Operational Costs: As counterintuitive as it may sound, working with specialized experts in the field, who have successfully implemented Big Data infrastructures multiple times, can lead to significant cost-savings in the long run. And when it comes to Big Data strategy, thinking about the sustainability and long-term viability of solutions is critical when embarking on projects of this magnitude.
• Faster Time to Market:Outsourced teams are designed to be agile and flexible. The right ones have the wealth of knowledge necessary to get the work done as fast as possible, bringing your Big Data projects to market in months rather than years.
• Reduced Risk: By choosing a Big Data partner well-versed in Big Data practices, including security at all levels, you can reduce the inherent risks associated with Big Data projects.

Challenges of Outsourcing Big Data Management

• Cultural and Communication Gaps: Outsourcing management and support can mean working with teams from different cultures that are located in different time zones, which can cause communication issues and misunderstandings. To solve these problems, companies can set up clear ways to communicate, arrange meetings when both teams are available, and train everyone to understand each other’s cultures better. This helps everyone work together more effectively and efficiently.
• Data Security Risks: Outsourcing Big Data implementations poses some risks to data security. When third parties handle sensitive data, there is always the possibility of exposure to threats such as unauthorized access, data theft, and leaks.To prevent such outcomes, it is crucial to maintain high-security standards, restrict data access to qualified personnel, and avoid sharing sensitive information via unsecured channels. (And of course, do some vetting and choose a partner with a solid reputation!)
• Dependency and Loss of Control: Relying too much on an external partner can lead to dependence and a loss of control over how data is managed. Good third-party partners will not gate-keep knowledge and will work to help teams understand what is happening in their Big Data infrastructure so they can make informed decisions about how the data is handled.

Final Thoughts

Implementing and supporting a Big Data infrastructure can be challenging for internal teams. Big Data technologies are constantly evolving, making it hard to keep pace. Additionally, storage and mining systems are not always well-designed or easy to manage, which is why it is best to stick with traditional architectures and make sure that clear documentation is provided. This makes the data collection process simpler and more manageable for whomever is overseeing it. 

When it comes to Big Data management, there is no “one size fits all” solution. It’s important to explore your options and consider hybrid approaches that give you data sovereignty and a high degree of control but also allow you to lean on the expertise of a third partner when necessary.

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Apache Kafka 4, the much-anticipated next major release of the popular event streaming platform, is almost here. In this blog, find out what’s changing in 4.0 and how to plan your next Kafka upgrade.

Apache Kafka Project Update

With four minor releases (3.6 through 3.9), several patches, and a major release on the horizon, 2024 has arguably been the most eventful in the history of the Apache Kafka project. The biggest development, of course, is the upcoming release of Kafka 4, which we will discuss more in depth later in this blog. First, let’s review the 3.x releases from this year that contained significant updates related to some of the key changes coming in 4.0.

Most of the 3.x updates have been made with the upcoming 4.0 Zookeeper deprecation in mind. ZooKeeper has been replaced by Kafka Raft (KRaft) mode and an official Zookeeper to KRaft migration process was introduced in 3.6 and designated as production ready in 3.7. Prior to 3.6, the only way to move to a KRaft-based Kafka cluster was a complete “lift and shift” process, which entailed installing a new KRaft-based cluster and then manually moving topics, producers, and consumers.

JBOD (Just a Bunch of Disks) support for migrating KRaft clusters also was added in 3.7, and some existing features got enhancements as well, such as improved client metrics and observability as defined in KIP-714 and early access to the next-gen consumer rebalancing protocol defined in KIP-848. Java 11 was also marked for deprecation in 3.7 and will be no longer be supported in 4.0.

With 3.8 and 3.9, Log4j appender was deprecated (and also targeted for removal in 4.0) and KIP-848 was promoted to preview status. There were also several improvements made to KRaft migration, and the quorum protocol implemented in KRaft. Support for dynamic KRaft quorums (as detailed in KIP-853) makes adding or removing controller nodes without downtime a much simpler process. With these improvements, Kafka 3.9 has basically become the de facto “bridge release” to 4.0.

Kafka 4 Release Date

According to the Kafka 4.0 release plan, feature freeze concluded on December 11th, 2024 and there is a planned code freeze on January 15th, 2025. This means Kafka 4 will likely come out in the final days of January or early February, as the code freeze is typically followed by a stabilization period lasting at least two weeks.

What’s Changing in Kafka 4

Based on the latter 3.x releases described above, we know that the biggest changes in Kafka 4 are removals, all noteworthy, though some more monumental than others.

Kafka Raft Mode (KRaft) Replaces ZooKeeper

The most notable change in Kafka 4 is that you can no longer run Kafka with ZooKeeper, with KRaft becoming the sole implementation for cluster management. While KRaft mode was marked as production ready for new clusters in 3.3, a few key pieces were needed before ZooKeeper deprecation and removal could be implemented. With the introduction and refinement of the migration process and JBOD support, the Kafka development community feels that total removal of ZooKeeper is finally ready with 4.0.

MirrorMaker 1 Removed

While not as huge of an architectural shift as the ZooKeeper removal, MirrorMaker 1 support is also going away in 4.0. Given that most organizations dropped  MirrorMaker 1 for MirrorMaker 2 quite some time ago, we expect this change to be less impactful to the Kafka ecosystem, but it is still notable nonetheless.

Kafka Components Logging Moving to Log4j2

With Log4j marked for deprecation in 3.8, 4.0 will also mark the complete transition from Log4j to Log4j2. After the Log4Shell vulnerability was disclosed in late 2021, an industry-wide effort to move to Log4j2 was put into motion. For this reason, most organizations already have moved off of Log4j, so while still a noteworthy change, it should not be all that impactful (and if you are still using Log4j, your systems are already most likely pwned at this point!).

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Kafka 4 Migration and Upgrade Considerations

There are definitely some considerations that should be taken into account when planning your KRaft migration. First, if this is your first foray into KRaft, don’t plan on retiring your entire ZooKeeper infrastructure anytime soon. Best practices dictate that organizations should be running dedicated controller nodes for production clusters, so your production infrastructure will most likely not change. For dev and integration/testing environments, running in mix-mode is fine, so you might see some infrastructure reclamation occurring in those environments.

Another major consideration is the upgrade path you will need to take. Since ZooKeeper is gone in 4.0, there will be no migration functionality associated with 4.0. So, for organizations still running Zookeeper on a Kafka version prior to 3.7, an interim upgrade to 3.9 would be required. Technically, with migration improvements introduced with 3.9, I’d recommend doing this interim step even for installations later than 3.7. The upgrade path would look something like:

3.x => 3.9 => ZK to KR migration => 4.0

Also of note is that Kafka 3.5 and later use a version of ZooKeeper that is not wire-compatible with version 2.4 and older. As such, for older Kafka clusters, a couple of additional interim steps will be required as well. You would need to upgrade to Kafka 3.4, and then upgrade the version of ZooKeeper to 3.8. That migration path might look something like this:

2.3 => 3.4 => ZK 3.8 => 3.9 => ZK to KR migration => 4.0

This should be an edge case since older versions prior to 2.4 should mostly be retired at this point.

What to Expect in Future Kafka 4.x Releases

If past precedence is any indication of future plans, I believe we will see continued improvements for containerization support and metrics collection, as well as refinements in the KRaft migration process. In regards to consumer performance, the full release of KIP-848 will also bring significant changes. Moving the complexity of the rebalancing protocol away from clients into the Group Coordinator, with a more modern event-loop process, creates a more incremental approach to rebalancing, where group-wide synchronization events will no longer be required for all coordination events.

Regardless, the future of Kafka looks pretty bright, with these enhancements likely to make the already popular event-streaming platform even better and more efficient.

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