The modern world depends a lot on data and information. And the more data there is, the harder it is to store, analyze, and process it. This is the problem that Databricks, an analytics platform based on the Apache Stark open-source product, which tries to combine data, AI, and analytics, solves.
What is data engineering?
Data engineering is the design and creation of systems for collecting, storing and analyzing large amounts of data. It is a broad field that has applications in almost every industry. Organizations can collect gigantic amounts of data, and they need people and technology to ensure that data scientists and analysts can apply that data.
Databricks doesn’t just simplify the work of these professionals, it gives a company an edge in a world where 463 exabytes of data will be produced per day by 2025, a unit followed by eighteen zeros.
What does Databricks do?
Databricks delivers a data storage and processing environment in which data scientists, data engineers and analysts can work.
It combines Data Warehouse and Data Lake methods.
A hybrid Data Warehouse and Data Lake approach involves processing unstructured and structured big data in a single cloud platform.
The Databricks platform consists of several layers.
Delta Lake is a storage layer, which ensures the reliability of data lakes. The layer can run entirely on an existing data lake, or connect to popular cloud storage services such as AWS S3 and Google Cloud.
Delta Engine is an optimized query engine to handle the data stored in Delta Lake.
The platform also includes several tools to support data science, BI reporting and MLOps.
All components are integrated into a single workspace. The interface can be customized for any cloud.
Databricks’ main competitor is called Snowflake. However, Snowflake uses the traditional “data warehouse” approach and was originally created as a cloud platform, while Databricks was distributed in an open core model.
The service’s counterparts also include Cloudera, Datastax, Qubole, MATLAB, Alteryx, Dremio and Intellicus.
Where can the data be obtained from?
Technical Director Matej Zaharia says that people switch to the Databricks platform for various reasons, but it is often dictated by business requirements, which are increasingly focused on cloud services.
Databricks co-founder and CEO Ali Godsey comments, “Almost every company has a data lake today. They try to extract information from it, but its value and reliability is often questionable. Delta Lake eliminates these problems – as evidenced by the interest of hundreds of businesses in this solution. Given that Delta Lake is open source, developers will be able to seamlessly create robust data lakes.”
Delta Lake data lake sits on top of (but does not replace) client storage and offers transactional level storage in both HDFS and Azure BLOB objects stored in cloud storage, such as S3. Users can download Delta Lake and combine it with HDFS in the on-premise version.
Data can be read from any storage system that supports Apache Spark data sources and written to Parquet, a storage format that Delta Lake understands. It was chosen because it was originally created for the Hadoop ecosystem and is independent of your choice of data environment. Delta Lake acts as a layer on top of the supported storage formats.