Many companies face various challenges in building a unified data platform. The more data appears, the more difficult it is to store, analyze, and process. Databricks solves this task with an analytical platform based on the open-source product Apache Stark. Databricks is an American data processing software company that gained fame as the developer of one of the versions of Apache Spark, a platform for distributed processing of unstructured and structured data. On its basis, Databricks created the Lakehouse Platform. Lakehouse Platform provides developers access to data management tools, including data lake, ETL, SQL solutions, AI tools for data science and machine learning. As all these frameworks are integrated into one place, the platform makes it easier to navigate and collaborate.
Databricks software is used in all industries where artificial intelligence and machine learning are applicable: education, retail, energy, games, media, and financial services. The company's product allows users to make forecasts of risks, demand, and investments, and gives the ability to use recommendation algorithms and automation.
Hybrid Architecture Lakehouse
Data Lakehouse is a mix of a data store and a data lake. They all have the same goal: creating native-cloud object storage based on a traditional data store and a data lake. The purpose of their deployment is to provide a single data source for the entire environment. Lakehouse characteristics include direct access to source data, standardized storage format, support for structured, semi-structured, and unstructured data, schema support, and simultaneous data reading and writing.
A Lakehouse brings the performance, management, and scale of storage to the data lake level and allows developers to have a single source of truth for all data. It can be deployed over an existing system. There is no need to introduce entirely new technology. Developers don't have to move data to downstream systems.
Businesses are becoming increasingly convinced of the benefits of the Lakehouse. The most significant value is consolidating isolated systems, gaining more excellent business value from data, expanding analytics to more progressive forms that include ML and AI, and providing a better basis for analyzing new and traditional data.
Fundamentals of the Databricks Lakehouse Platform
What are the Main Services the platform proposes?
• It provides reliable management, reliability, the performance of data warehouses, flexibility, openness, and support for machine learning (ML) of data lakes.
• It is developed based on open standards and open-source standards for maximum flexibility.
• The multi-cloud platform's overall security and data management approach helps users work more efficiently and seamlessly to innovate.
• Users can easily share data and create modern data stacks thanks to unlimited access to more than 450 partners across the data landscape.
• The platform provides a collaborative development environment for data teams.
What is Delta Lakehouse?
The Databricks platform consists of several layers.
Delta Lake is a storage layer that ensures the reliability of data lakes. The layer can run entirely on an existing data lake or connect to popular cloud storage such as AWS S3 and Google Cloud.
Delta Engine is an optimized request processing system for working with data stored in Delta Lake.
Also, several tools are built into the platform to support data science, BI reporting, and MLOps. All components are integrated into a single workspace.
Developers can create reliable data lakes without hindrance because Delta Lake is open source. It is located on top of the customer's storage system (but does not replace it) and offers a transactional storage tier in HDFS and Azure BLOB formats stored in a cloud warehouse.
Another advantage of Delta Lake is accessing early versions of data for reconciliation, rolling back transactions, and reproducing machine learning experiments.