Metadata: Data warehouses and data lakes typically offer a way to manage and track all the databases, schemas, and tables that you create.Three key differences between a data warehouse and a data lake are how they provide storage, compute power, and metadata (contextual information about the data in your ecosystem).
Data lake vs data warehouse: 3 key differencesĭata lakes and data warehouses are both data storage repositories. What is a data lake?Ī data lake is a data repository that provides storage and compute for structured and unstructured data, oftentimes for streaming, machine learning, or data science use cases. Whether you’re just getting started or are in the process of re-assessing your existing big data solution, here’s everything you need to know to choose the right data lake or data warehouse for your data stack: What is data warehouse?Ī data warehouse is a data repository that provides data storage and compute, usually leveraging SQL queries for data analytics use cases. Add data lakes such as S3 or Databricks to the mix, and the decision between data lake vs data warehouse becomes that much harder. With the release of Amazon Redshift in 2013 followed by Snowflake, Google BigQuery, and others in the subsequent years, the market has become increasingly hot. When it comes to selecting between a data lake vs data warehouse for your data platform, however, the answer isn’t as straightforward. Companies literally can’t use data in a meaningful way without the a data lake vs data warehouse discussion. In many ways, the cloud makes data easier to manage, more accessible to a wider variety of users, and far faster to process. Now, with the rise of data-driven analytics, cross-functional data teams, and most importantly, the cloud, the terms “ modern data warehouse” or data lake are nearly analogous with agility and innovation. These bastions of the office basement were long associated with siloed data workflows, on-premises computing clusters, and a limited set of business-related tasks (i.e., processing payroll, and storing internal documents). Twenty years ago, your data warehouse probably wouldn’t have been voted hottest technology on the block. Now, we share everything you need to know about the foundation of your data infrastructure: data lake vs data warehouse.
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It’s clear this debate isn’t going anywhere, but the technologies are evolving - fast. In the first article in our data platform series, we discussed how to approach building your data platform like a product. The old battle lines around “raw vs processed data” or “data engineer vs data scientist” are fading and new differentiators are emerging. The data lake vs data warehouse debate is heating up with recent announcements at Snowflake Summit including Apache Iceberg and hybrid tables on one side, and the metadata related announcements at Databrick’s Data + AI around the new Unity Catalog.
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