Databases have undergone many changes in recent years, and today there are more advanced options for storing information than the classic data warehouses. Now there is a trend of Data Lake. What are the benefits of the data lake, how can it be developed in the cloud?
The world of databases has changed beyond recognition in the last decade. Until recently they were among the most stable components in development projects, the number of players was (relatively) small and it was easy to choose between them. But a lot of changes have taken place since the first “not only SQL” databases (no-SQL) appeared, and they did not miss the databases (the part of databases that form the basis for analytical systems). One of the developments in the field of large data storage is the data lake.
What is a data lake?
The data lake is a form of data storage that aims to gather all the existing information in the organization in one central database. The emphasis is on transferring all the data without distinction in their form or format. There are three main types of data: structured data that are usually in a relational database; Semi-structured data such as xml, json documents; And unstructured data such as e-mail or other content files.
All this data is collected in the database in its original form, without modification or adjustments. When the data is accessible, processing processes can be activated to enable various activities and tasks such as report generation, visualization and more advanced activities such as machine learning. The name data lake symbolizes the fact that the information should appear in its natural form and flow freely from place to place as needed.
Data lakes have begun to grow thanks to several technological developments, most notably big data technology and especially thanks to Snowflake Outsourcing. This technology enables the processing of mass data in any form (including unstructured data) and quickly. Today it is possible to perform map-reduce processes in memory, save a lot of time and connect and analyze a lot of diverse information.
The big data makes it easy to load the data into a columnar database, or allow access via the SQL language directly over the files. Another reason for the growth of data lakes is related to lowering storage costs – storing all the information of an organization requires a lot of storage space, and therefore reducing costs is a critical step in the development of the data lake. It is now also clear that the data lake is an advanced option for information retention.
Why there was a need of Data Lake?
The data lake was created to overcome problems related to classic data warehouses. Data warehouses have been around for decades and will continue to accompany us for a long time, but alongside their benefits, they contain some understandable issues. One of the main ones is that data warehouses are defined by groups of different databases. Most of the data are purely relational. Each of these groups has been defined in the original system separately and can usually not be linked. In fact, each of the groups behaves like a silo. In contrast, the data lake contains not only relational data, and because the data comes in its simplest form, it is easier to connect them. Unstructured data is a huge source of information that today’s organizations cannot do without.
