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Data Lake Best Practices You Need to Follow

SwoopTalent
June 3, 2019

For organizations dealing with big data in many different format structures and levels of preparation, a data lake may be the best means for data storage. It may not be the most attractive method, but it is certainly the most efficient, and the most broad. Ingestion into a data lake is quick, and allows you to hold your structured and unstructured data, internal and external data, and enable teams across the business to discover new insights.

Sounds great, right? But don’t let the hype fool you into thinking this is something you can implement with a flick of your wrist. Execution and maintenance of a data lake requires strategy and some serious elbow grease -- but when done successfully, the rewards are enormous. Below, we’ve outlined seven top tips for data lake best practices, to help you avoid making mistakes, and navigate problems which may arise along the way.

Data Lake Best Practices

  1. Start with a business problem or use case. Successful implementation of a data lake system usually starts with a need. Identify business problems or use cases where a data lake is the ultimate and most sensible solution, and go from there. Without structure or grounding, data lakes may morph into idealistic science projects where the expected result is a universal solution to company-wide use cases. Most efficient utilization of a data lake begins with a specific problem, and is carried out through focused and persistent application.
  2. Buy or build? To suit the needs of any organization, a data lake will typically feature a little of both. A vendor solution will rarely offer a system which meets all of your data storage requirements. However the cost of time and labor means a data lake is far too expensive to build from scratch. Begin by sourcing a pre-existing product which comes the closest to addressing the requirements of your organization, factoring in ease of navigation and customization. Then, modify the product to reduce cost and maximize the usefulness of your new system.
  3. Quickly onboard and ingest data, with minimal upfront improvement. The big benefit of data lake is early ingestion and late processing. This is similar to ELT strategies, only transformation often occurs much later in time, and often as data is read. Implementing this practice allows integrated data to be available almost immediately for analytics, operations, and reporting. A diverse ingestion repertoire enables an organization to scale and simplify the onboarding process of a greater volume of raw and structured data
  4. Lock down who loads which data into the lake, and when. Data lakes may become chaotic and disorganized without implementation of a specific onboarding methodology. Employing a steward or curator for enforcement purposes helps to mitigate any violations of a data lake anti-dumping policies. Erect standards of data documentation using metadata, an information catalog, or business glossary to optimize user comprehension and navigability, and reduce data redundancy. Allow exceptions to policy rules -- such as when a data analyst or scientist dumps data into analytics sandboxes.
  5. Source the correct resources for data lake operation. On a related note to point 3, staffing team members with the appropriate knowledge and skills for data lake operation is essential. The data lake hype is alive and well, an easier imagined than done. Like we said, successful data lake utilization takes hard work and comprehension. And to do this well, you need staff who know what they’re doing. Decide whether there are any potentials in-house with the proper exposure and experience. If not, source outside hires with comprehensive and verifiable training to get you started on this project. Then, expand the data team by involving in-house users in your data quality process.
  6. Use your data lake to fulfill multiple technical and architectural purposes. Typically, a single lake is able to address a myriad of architectural problems. These can include data staging and landing, archiving for detailed source data, sandboxing for analytics data sets, and managing operational data sets. Be sure that, even while broadening a single data lake’s actions, you define unique storage and processing characteristics if it is distributed over multiple data platforms.
  7. Don’t forget to lock the door behind you. Data security should always be a forward priority for an organization. We’ve pretty much figured out effective safeguarding systems for data warehouses and other, more tried-and-true methods of data storage. But data lakes are new, and break-ins are unfortunately common as companies forget to modify their security policies and data encryption strategies upon implementation. Scrutinize your vendors to see just how they are addressing security issues, and implement these essential checks to keep your data safe and secure: user authentication, user authorization, data in motion encryption, and data at rest encryption.

Implementing a data lake system isn’t easy, but it doesn’t have to be hard. Use our seven top tips to get you started on constructing the data repository of your dreams.

Need more insight into making the most of your data? Check out our other article, HR Analytics Courses You Need to Take This Year.

The SwoopTalent platform automatically connects all of your talent systems and data to power everything you need to do across the full talent lifecycle. Whether it's integrations, automation, data refresh, analytics, migrations, machine learning or any other priority, we free you up to do the strategic things. To find out how SwoopTalent can help your organization, request a demo here.

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