{"id":20945,"date":"2021-09-24T23:13:21","date_gmt":"2021-09-24T16:13:21","guid":{"rendered":"https:\/\/renovacloud.com\/?p=20945"},"modified":"2024-12-02T17:27:25","modified_gmt":"2024-12-02T10:27:25","slug":"the-rise-of-cloud-data-warehouse","status":"publish","type":"post","link":"https:\/\/renovacloud.com\/en\/the-rise-of-cloud-data-warehouse\/","title":{"rendered":"THE RISE OF CLOUD DATA WAREHOUSE"},"content":{"rendered":"

The rise of data-driven analytics, cross-functional data teams, and most importantly, the cloud, the phrase \u201ccloud data warehouse\u201d is nearly analogous with agility and innovation. In many ways, the cloud makes data easier to manage, more accessible to a wider variety of users, and far faster to process.\u00a0<\/span><\/p>\n

When it comes to selecting the right cloud data warehouse for your data platform, however, the answer isn\u2019t as straightforward. With the release of Amazon Redshift in 2013 followed by Snowflake, Google Big Query, and others in the subsequent years, the market has become increasingly hot. <\/span>Add\u00a0<\/span>data lakes<\/span><\/a>\u00a0to the mix, and the decision becomes that much harder<\/span>!<\/span><\/p>\n

Whether you\u2019re just getting started or are in the process of re-assessing your existing solution, here\u2019s what you need to know to choose the right data warehouse (or lake) for your data stack:<\/span><\/p>\n

What makes a data warehouse\/lake?<\/span><\/h3>\n

Data warehouses and lakes are the foundation of your data infrastructure, providing the storage, compute power, and contextual information about the data in your ecosystem. Like the engine of a car, these technologies are the workhorse of the data platform.<\/span><\/p>\n

Data warehouses and lakes incorporate the following four main components:<\/span><\/p>\n

Metadata<\/span><\/h4>\n

Warehouses and lakes typically offer a way to manage and track all the databases, schemas, and tables that you create. These objects are often accompanied by additional information such as schema, data types, user-generated descriptions, or even freshness and other statistics about the data.<\/span><\/p>\n

Storage<\/span><\/h4>\n

Storage refers to the way in which the warehouse\/lake physically stores all the records that exist across all tables.\u00a0<\/span><\/p>\n

Compute<\/span><\/h4>\n

Compute refers to the way in which the warehouse\/lake performs calculations on the data records it stores. This is the engine that allows users to \u201cquery\u201d data, ingest data, transform it \u2014 and more broadly, extract value from it. Frequently, these calculations are expressed via SQL.<\/span><\/p>\n

Why choose a data warehouse?<\/span><\/h3>\n

Data warehouses are fully integrated and managed solutions, making them simple to build and operate out-of-the-box. When using a data lake, you typically use metadata, storage and compute from a single solution, built and operated by a single vendor.<\/span><\/p>\n

Unlike data lakes, data warehouses typically require more structure and schema, which often forces better data hygiene and results in less complexity when reading and consuming data.<\/span><\/p>\n

Owing to its pre-packaged functionalities and strong support for SQL, data warehouses facilitate fast, actionable querying, making them great for data analytics teams.<\/span><\/p>\n

Common data warehouse technologies include:<\/span><\/h5>\n