Data lakes and data warehouses

Data retention is an important part of an organization’s information management. Data lakes and data warehouses are used for this purpose. The main difference between them is how the data is stored.

Data lake is a vast body of raw and unstructured data whose purpose has not yet been defined. A data warehouse is a store of structured, filtered data created for a specific purpose.

Data Lake

Data lake acts as a hub that centralizes organization data collected from a variety of sources into a single logical platform, enabling the consistent management of large amounts of data.

It can store any data, from unstructured data such as text documents or images to semi-structured, such as hierarchical web content  or strictly structured, such as rows and columns of relational databases.

Data lakes are best suited for organizations that need to provide a lot of data internally and externally. This way of storing information allows you to:

  • Reduce resources: traditional systems try to fit everything into one model, wasting time processing unused data. In a data lake, such a need for processing arises when the information is used.
  • Access data: a right to access data is granted.
  • Increase efficiency: data sets do not need data to be defined schematically, making data transfer, design, and planning processes simpler and faster.

Technologies we use:

  • Microsoft Azure Data Lake Analytics;
  • Microsoft Azure Data Lake Storage;
  • Red Hat Open Data Hub;
  • Apache Hadoop;
  • Apache Kafka;
  • Apache Spark;
  • Apache Superset;
  • JupyterHub.

Platforms we use:

  • Amazon Cloud;
  • Microsoft Azure;
  • IBM Cloud.

Data warehouse

In a data warehouse, information from many sources is stored by merging it into single cubes and, if necessary, is transformed and analyzed in various sections.

For example, an organization stores information about employees, their salaries, products created, customers, sales, and invoices in a data warehouse. When asked about cost-cutting measures, the situation will require an analysis of all this data.

The ability to make operational decisions based on different processed data elements is a key data warehouse service.

Thus, a data warehouse can be called an analytical data repository, in which structured data is stored in multidimensional data cubes. A data warehouse collects and stores data from one or more sources so that it can be quickly analyzed to gain business insights. They are described before the analysis to make it extremely fast.

Technologies we use:

  • Microsoft SQL Server;
  • Microsoft SQL Server Analysis Service;
  • Microsoft SQL Server Integration Services;
  • Microsoft SQL Server Reporting Services;
  • Oracle Database;
  • Oracle Data Integrator.