Data Swamp vs Data Lake
When you imagine a lake, it’s likely an idyllic image of a tree-ringed body of reflective water amid singing birds and dabbling ducks. But a swamp, on the other hand, is dark and dank, full of scary creatures, heavy wet air, and either a poisonous frog or an angry alligator behind every dead tree snag.
I’ll take the lake, thank you very much. And so will your data.
You know the story well: you have a ton of data and need fast access to the right data. Building an efficient solution for data storage and processing is becoming more than just a back-office or IT challenge. Data is the raw material for the modern business apparatus. When data is clean, robust, and flowing free, your business will thrive. But when it’s dirty, stagnant, or hard to unleash, your business will suffer.
Many organizations have built a data lake to solve their data storage, access, and utilization challenges. A data lake is a centralized repository used to store data of many types at enterprise scale, which then enables easy access for many business needs. Unfortunately, data lakes can quickly become data swamps or dumping grounds where data becomes harder to find, evaluate, or use. Keeping your data lake clean, organized, governed, and understandable is how to prevent it from becoming a data swamp.
Benefits of a Data Lake
Data lakes empower business users to find relevant information faster, regardless of sources or locations. This then enables more effective analyses, deeper cross-organization collaboration, and faster, more informed decision making. But, on the back end, data lakes give businesses a common repository to collect and store data, streamlined usage from a single source, and access to the raw data necessary for today’s advanced analytics and artificial intelligence (AI) needs. Data lakes also support the growing thirst for analysis by data scientists and data analysts, as well as the critical role of data governance.
When businesses are utilizing a data lake efficiently and effectively, they have the power to expand business intelligence by using more data from more sources to glean more impactful insights. But setting up a data lake takes a thoughtful approach to ensure it’s positioned to prevent it from becoming a data swamp. That starts with a modern data management architecture built on an enterprise scale platform, and that provides easy access for business users.
Signs Your Data Lake is Actually a Data Swamp
There are a few clear signs your data lake is turning into a data swamp.
A key difference between a data lake and data swamp, as well as a physical lake and swamp, is cleanliness. Dirty data tends to muck up every other downstream action or process, and it’s a clear warning sign your data lake is turning into a data swamp. As data ages, it not only becomes irrelevant, it can become inaccurate, duplicative, or misleading due to unreflected changes. That dirty data then corrupts analyses and forces mistakes. A frequent and periodic data cleansing strategy is called data auditing. This involves using statistical methods to detect anomalies and contradictions in the data, which leads to a clear picture of the kinds of anomalies that occur and where they dwell.
Lack of metadata
A lack of organization is another sign of a data swamp, typically driven by bad or incomplete metadata. A lack of metadata prevents data curation, blocks any active data management, and impedes fast and accurate data governance. It obscures the context behind the data, rendering it virtually unusable by the business users who need it.
Data swamps are also characterized by too much unknown, irrelevant, or unnecessary data. Just as lakes benefit from the filtering power of surrounding rocks, roots, and soil to sift out incoming impurities, data lakes benefit from a diligent effort to prevent them from becoming a dumping ground for all and any data.
Data governance helps keep data quality high and data literacy efforts on track. Poor or nonexistent data governance, however, leads to data that’s misused, held too long, or otherwise corrupts your data-driven processes. It’s wise to follow sound methods of data governance as your data lake grows so that it does not become a data swamp.
Lack of automation
Automation is especially helpful in keeping data lakes from becoming data swamps. If you’re not using automated data maintenance and cataloging practices, it’s likely that your efforts won’t be able to maintain pace with your growing data lake.
So what is a Data Lakehouse?
At the risk of pushing this lake metaphor too far, a new approach to managing your data lake is through a data lakehouse. A data lakehouse combines the benefits of a data lake, including scale, efficiency, and flexibility, with the benefits of a data warehouse, which include ideal support for structured data. By using the structure of a data warehouse on a data lake, your business users can have easy, streamlined access to comprehensive data.
A data lakehouse treats all underlying data, whether from a data lake or a data warehouse, equally in the eyes of a business user, business intelligence solutions, and even AI applications. This enables the best of both worlds, but does so using a modern, open architecture.
Alation & Your Data
A data lake is the best way to enable fast, efficient, and impactful reporting, visualization, analytics, machine learning, and more from your vast stores of data. As they become integral to your data strategy, it becomes even more important to prevent them from becoming a data swamp. A data catalog leverages metadata to help you filter out irrelevant data, improve data governance efforts, and add automation to your data lake maintenance and management. Learn how Alation Data Catalog works to keep your data lake from becoming a data swamp.