Accelerate Your Data & AI Outcomes with Databricks

The history of data warehousing (DW) began with assisting business executives in gaining analytical insights, by collecting data from operational databases using Extract, Transform, Load (ETL) into centralized warehouses, which could subsequently be used for decision support and business intelligence (BI). Data in these warehouses would be written with schema-on-write, ensuring that the data model was optimized for downstream BI consumption.

However, as time passed, new requirements and data sets emerged, posing new challenges to the data warehouse, such as

Then, Data Lakes came to the rescue.

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to make better decisions.

The major issue with a data lake architecture is that raw data is stored with no control over the content. To make data usable, a data lake must have specified processes for cataloging and securing data. Without these components, data cannot be identified or trusted, resulting in a data swamp. To meet the needs of a broader audience, data lakes must have governance, semantic consistency, and access controls.

Some of the challenges when working with data lakes are

A Data Lakehouse unifies the best of data warehouses and data lakes in one simple platform to handle all your data, analytics and AI use cases. It’s built on an open and reliable data foundation that efficiently handles all data types and applies one common security and governance approach across all of your data and cloud platforms.

Evolution of data platforms

The Databricks Lakehouse Platform combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes.

This unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science, and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security, and governance helps you operate more efficiently and innovate faster. Refer Delta Lakehouse Platform for more information.

A lakehouse has the following key features:

• Transaction support: Many data pipelines will be reading and writing data at the same time in an enterprise lakehouse. Support for ACID transactions provides consistency when several parties, often using SQL, view or write data at the same time.
• Schema enforcement and governance: The lakehouse should be able to handle schema enforcement and evolution, as well as DW schema paradigms like star/snowflake schemas. The system should include solid governance and auditing systems, as well as the ability to reason about data integrity.
• BI support: BI tools can be used directly on the source data using Lakehouses. This lowers the cost of having to operationalize two copies of the data in both a data lake and a warehouse by reducing staleness and improving recency, reducing latency, and lowering the cost of having to operationalize two copies of the data.
• Storage is decoupled from compute: In practice, this implies that storage and computing clusters are separated, allowing these systems to expand to a large number of concurrent users and larger data sets. This is also a feature of certain current data warehouses.
• Openness: The storage formats used by Lakehouse are open and standardized, such as Parquet, and they provide an API so a variety of tools and engines, including machine learning and Python/R libraries, can efficiently access the data directly.
• Support for diverse data types ranging from unstructured to structured data: The lakehouse can be used to store, refine, analyse, and access data types required for a wide range of new data applications, such as photos, video, audio, semi-structured data, and text.
• Support for diverse workloads: Data science, machine learning, and SQL analytics are all included. Although multiple tools may be required to support all of these workloads, they all share the same data repository.
• End-to-end streaming: In many businesses, real-time reports are the norm. Streaming support eliminates the requirement for separate systems to serve real-time data applications.

Databricks SQL

Machine Learning

Built on an open lakehouse architecture, Databricks Machine Learning empowers ML teams to prepare and process data, streamlines cross-team collaboration, and standardizes the full ML lifecycle from experimentation to production. Below are the components of Databricks Machine Learning.

Refer Databricks Machine Learning for more information.

Databricks Engineering

The Databricks Lakehouse Platform is an end-to-end data engineering solution that automates the complexities of designing and managing pipelines and running ETL workloads directly on a data lake, allowing data engineers to focus on quality and dependability to produce useful insights. Below are the specialties of Databricks engineering

Refer Data Engineering with Databricks for more information.

We have incredibly skilled Databricks specialized consultants who can help you implement data and AI platforms on Databricks or help you migrate your existing data platform to Databricks from start to finish.

If you’re looking to implement Databricks platform for your Organization and need someone to help you from start to finish, talk to one of our experts.