Since the data lake concept emerged more than a decade ago, data lakes have been pitched as the solution to many of the woes surrounding traditional data management solutions, like databases and data warehouses. Data lakes, we have been told, are more scalable, better able to accommodate widely varying types of data, cheaper to build and so on.
Much of that is true, at least theoretically. In reality, the way many organizations go about building data lakes leads to a variety of problems that undercut the value their data lakes are supposed to deliver.
Those challenges don’t mean that data lakes are a fundamentally bad solution. You can and should use data lakes. But if you want to reap all the rewards that data lakes have to offer, you should rethink your data lake architecture and management.
Here’s your quick rundown on common data lake woes as well as tips on how to solve them by revamping your data lake management.
When you have lots of data of differing types to manage or analyze, data lakes make it easy to do so -- at least in theory.
The data inside data lakes can vary widely in size and structure, not needing to be organized in any particular way. A data lake can also accommodate as little or as much data as you need, and it can grow quickly in size as you add more data. This is why many companies use data lakes to aggregate data from multiple sources. Furthermore, they’re often chosen over data warehouses, which require data to be structured and organized in particular ways, making them less flexible and scalable.
Unfortunately, it’s easy for data lakes to become data swamps, without the proper documentation and governance, including metadata management. That’s only one of the data lake advantages and disadvantages that should be carefully weighed, before investing in a data lake strategy. Let us explain.
Data lakes can be very expensive to implement and maintain. Although some cloud data platforms, like Hadoop, are open source and free of cost if you build and manage them yourself, doing so often takes months and requires expert (read: expensive) staff.
Managed cloud data platforms may be easier to deploy, but they are still difficult to manage -- and they come with steep fees.
How to avoid it: You probably already use cloud storage, such as Amazon S3, as a data store or repository. Leveraging these resources for building a data lake can make the process of ingesting and retaining data far less costly. This will enable your data science and engineering teams to access the data they need to conduct thorough long-term analysis for issues to achieve observability at scale.
Read more about AWS data lake best practices in this post.
Collecting data is only the first step. Even for skilled engineers, data lakes are hard to manage. Ensuring that your host infrastructure has the capacity for the data lake to keep growing, dealing with redundant data, securing all of the data and so on are all complex tasks, whether you’re using a vanilla open-source cloud data platform or a managed service.
How to avoid it: Enterprise data engineers often create ETL pipelines to move data into query for analysis. That’s because keeping your data in hot storage can become costly within many systems. However, if you are using AWS S3 or low-cost cloud object storage, you can avoid having to move data in order to analyze it, reducing the load on data engineering resources and enabling self-service analytics.
Even after you spend months setting up your data lake, it will often be years before it grows large enough and becomes sufficiently well integrated with your data analytics tools and workflows to deliver real value.
How to avoid it: Many BI and analytics tools can be integrated directly with data lakes to generate a faster time to value. In fact, you can integrate BI and data visualization platforms with a data lake, which feature tools that help clean, transform, and prepare unstructured data for business intelligence analytics. Embracing streaming analytics can turn on real-time data lake capabilities, helping your team achieve faster time to insights.
Conventional cloud data platforms are good at storing data, but not so good at securing it and enforcing data governance rules. You’ll need to graft on security and governance. That translates to even more time, money and management headaches.
How to avoid it: The sheer amount of data in your data lake means that you should embrace data quality best practices. Effective data management practices include establishing data validation rules, tracking data lineage, and defining policies for data access, retention, and deletion.
Engineers with real expertise in setting up and managing data lakes aren’t exactly a dime a dozen. In fact, there is an ongoing skills shortage for both data scientists and data engineers.
How to avoid it: While it’s difficult to spin up new data science or machine learning talent on demand, it is possible to select tools that enable self-service among the engineers or other team members who will use the analytics system most often. Invest in building a culture of data literacy so that more team members are aware of these systems and how to use them.
Data is growing faster than compute power. This means that data lakes are getting bigger and bigger, but the computers required to host and manage them aren’t getting more powerful at the same rate.
The result: Without an efficient way to manage data within data lakes, businesses will end up paying more and more for the compute resources they need to handle their data.
How to avoid it: Some data lake strategies can actually reduce costs and manage compute resources more efficiently. For example, for some security use cases, it may make sense to embrace a modular security data lake alongside purpose-built tools such as a SIEM, to keep costs in check.
In a word, No. While these products were imagined as solutions to the data lake and data management challenges businesses face today, they fail to solve most of the core problems surrounding data lakes: They are difficult and costly to manage. There is still a serious skills gap surrounding them, they require massive (and hence expensive) computing power.
As a result, these open-source solutions have failed to “democratize data” and allow every business -- not just the hyperscalers -- to build massive data lakes.
Think of open source as just one option as you are architecting your data lake. Many teams choose to leverage open APIs and open source components to build an observability solution, rather than relying on costly, all-in-one solutions. If you are looking to save costs with open-source tools, it’s important that they are easy-to-use and integrate well with your existing technology stack.
All of the above adds up to a world in which businesses are spending more and more to build and manage their data lakes, while receiving less and less value from them. Data lakes can do a good job of storing data at scale in a flexible way, but data lakes alone cannot turn data into value -- far from it.
Remember to achieve full value from your business data you’ll need to: