Your business has more incoming data than ever before, from structured transactional records to clickstream data — and its volume increases every day.

This presents a challenge: deciding what data you’ll keep and how to store it to effectively power your current and future analytical needs. 

A data lake is an increasingly popular solution that allows you to store all your data in a centralized location while maximizing agility 

What is a data lake?

A data lake is a central storage location or repository that stores all your data in its raw format at any scale [1].

It’s a deceptively simple concept, so it’s worth examining aspects of this definition in more depth.

  • A central location — a data lake acts as a single source of truth for all of your business’s data needs. You can use data stored in a data lake to power real-time and batch analytics, machine learning, visualizations, and dashboards. Your business has many data sources and use cases. A data lake acts as the central hub that connects your growing infrastructure.
  • Stores all your data at any scale — data lakes are made scalable and affordable by distributed and cloud storage solutions. There’s no need to filter which data you store as it comes in or discard older data to save space. This means your data lake can be a truly complete data record.
  • Stores raw data  — data is kept in its native format, no matter what that may be. The same data lake can contain structured relational data (data stored in rows and columns), unstructured data such as emails or PDFs, streamed data from social media and IoT devices, and everything in between.

Implementing a data lake has been shown to improve how organizations use data, which in turn increases business performance. According to an Aberdeen Group survey, businesses with a data lake saw a doubling of their data quality and a 9% increase in organic revenue growth [2].

Data lake vs data warehouse

A data warehouse is another common method used by organizations to store large amounts of data. Though they are often discussed together, data lakes and data warehouses have some important differences.

Data warehouses are highly structured and curated data repositories. Unlike data lakes, they have a pre-defined schema. This means that significant effort is put into designing a highly tailored data model for a specific purpose. Incoming data is transformed to fit the schema prior to storage. If it doesn’t fit within the schema, it isn’t stored [3]. 

While data warehouses are great for staying organized and performing query-based, batch analytics on predictable types of data, they lack flexibility. When your data analysis methods or goals change, you’ll need to redesign your data warehouse.

In contrast, data lakes store all incoming data in any format. You apply schemas and transformations when you extract the data for analysis. This allows different teams throughout your organization to perform a variety of operations from the same source. 

Data lakes can power traditional batch and SQL analytics, as well as real-time analysis, machine learning, and predictive analysis. Storing raw data makes data lakes a better source for analysts and engineers, which in turn allows your organization to implement new analytical practices quicker.

What a data lake should and shouldn’t be

When implemented correctly, a data lake provides a simple, flexible source of truth for your organization’s many types of data. It can power a variety of real-time and batch analyses across different platforms and tools. 

On the other hand, because of the unstructured nature and virtually endless storage of data lakes, it’s easy for organizations to use them as dumping grounds for data. Without proper planning, lakes can degrade into “data swamps” over time, where data of ambiguous quality sits unused.

Because data lakes don’t demand a schema upfront, as data warehouses do, it’s up to your organization to manage your data lake and the quality of its contents. It’s also critical to include an access control system to keep data secure while granting access to the appropriate analysts and user groups. These are essential components of data governance

Estuary Flow helps you reap the benefits of a data lake without the danger of disorganization. Backed by cloud storage, Flow allows for either rigorously defined or relaxed schemas, bridging the gap between data lakes and data warehouses.  We prioritize flexibility and centralization not just for data storage, but as part of a larger, end-to-end data pipeline.

Sources

[1] https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/ 

[2] https://s3-ap-southeast-1.amazonaws.com/mktg-apac/Big+Data+Refresh+Q4+Campaign/Aberdeen+Research+-+Angling+for+Insights+in+Today’s+Data+Lake.pdf

[3] https://www.blue-granite.com/blog/bid/402596/top-five-differences-between-data-lakes-and-data-warehouses

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