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How to Create a Well Designed Data Lake Architecture?

By: Ojas Mulay
October 13, 2022
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Data lakes deliver unprecedented agility

A data lake is an essential tool for big data analytics. A key advantage of developing a data lake is making data accessible in a way that can impact business change as quickly and efficiently as possible.

When comparing data lake architecture vs. traditional databases and data warehouse, the focus is on agility. Rather than merely reporting and running business intelligence solutions on existing business operations in a data warehouse or analyzing how the business is performing, a data lake gives organizations the opportunity to rapidly reevaluate where the business should be innovating.

The foundation of a data lake is a storage system that can accommodate all of the data across an organization: from supplier quality information, to customer transactions, to real time product performance data. Unlike a data warehouse or an analytics database, a data lake can be used to collect any type of data without knowing the details such as the schema or the meaning in advance.

Building a robust data lake architecture

Building a data lake architecture requires a concerted focus in following areas: data collection and transformation (i.e.physical storage, data processing ETL/ELT), metadata layout (i.e. table formats, data security), and access as well as mining the data lake (using a semantic/data virtualization layer). We outline our thoughts of a well designed data lake architecture below.

Cloud data lakes offer ultimate flexibility in data collection and transformation

When we think about a data lake, it might be used to combine information about the lifecycle of each product, enabling the business to analyze what level of quality from a given supplier leads to the highest customer complaints. It’s promising!

In terms of a good data lake design that’s built on an infinitely scalable storage starts with a data collection system that can accept any type of data e.g object stores. Further, best practices when building a data lake architecture include governance and lineage tools to combine and transform datasets.

What was traditionally a simple, linear Extract, Transform, and Load (ETL) process to collect data into a data warehouse is now an Extract, Load, and Transform(ELT) with a data lake.

Let me break it to you gently, the processes should be renamed: Collect.

Leveraging open data table formats to handle large data volumes

Speaking of collecting large volumes of data, leveraging modern table formats like Apache Iceberg and Delta Lake, which are specifically built for the cloud can address the drawbacks of legacy metadata stores.

They handle large volumes of data that reside in the data lake and improve overall performance. It also provides additional capabilities like ACID (atomicity, consistency, isolation, and durability), which can unlock additional use cases on the data lake. In addition, enabling data access security on the lake ensures the data compliance policies of the organization.

Data virtualization enables effective access and analytics

So far, we’ve addressed data collection, open table formats and access, a well designed system also empowers users with data access as soon as it arrives.

First generation data lakes loaded data that was transformed in the data lake into a data warehouse. Even today, separate analytics systems are common add-ons to a data lake. These transitory architectures are helpful in adding a data lake to an existing data management system, but fail to deliver on the speed and efficiency that’s possible with a well designed data lake.

A modern data lake architecture adds a third layer of data virtualization, where the analytics engine operates directly on the data lake instead of using an add-on legacy system.

A data lake with data virtualization gives users a single location with direct access to any data for which they’re authorized. Rather than modeling data, then transferring it from the data lake to a third party system, and managing permissions and access controls in multiple locations and maintaining data consistency, a data lake with data virtualization offers a true single source of truth.

The data virtualization enabled data lake architecture, with a query engine directly running on the data lake, delivers organizations the ultimate flexibility and allows for the agility necessary to support innovation.

Starburst delivers data virtualization for a complete data lake architecture

Starburst enables data virtualization by providing the ability to query data across lakes and other sources and helping organizations realize value from well designed data lake architectures. By using fast and efficient query engines on top of the flexible transformation and scalable data collection capabilities of a data lake, organizations can deliver data driven processes to impact their businesses as well as support reporting and business intelligence systems.

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Ojas Mulay

Solutions Architect, Starburst

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