Birst vs Databricks comparison

Birst Networked BI and Analytics eliminates information silos. Decentralized users can augment the enterprise data model virtually, as opposed to physically, without compromising data governance.

A unified semantic layer maintains common definitions and key metrics.
Birst’s two-tier architecture aligns back-end sources with line-of-business or local data. Birst’s Automated Data Refinement extracts data from any source into a unified semantic layer. Users are enabled with self-service analytics through executive dashboards, reporting, visual discovery, mobile tools, and predictive analytics. Birst Open Client Interface also offers integration with Tableau, Excel and R.

Birst goes to market in two primary ways: as a direct sale, for enterprises using Birst on internal data to manage their business; and embedded, for companies who offer analytic products, by embedding and white-labeling Birst capabilities into their products.

Birst’s is packaged in 3 available formats: Platform and per-user fee; by Department or Business Unit; by end-customer (for embedded scenarios).

Databricks is an industry-leading data analytics platform which is a one-stop product for all data requirements. Databricks is made by the creators of Apache Spark, Delta Lake, ML Flow, and Koalas. It builds on these technologies to deliver a true lakehouse data architecture, making it a robust platform that is reliable, scalable, and fast. Databricks speeds up innovations by synthesizing storage, engineering, business operations, security, and data science.

Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform. This enables users to easily manage a colossal amount of data and to continuously train and deploy machine learning models for AI applications. The platform handles all analytic deployments, ranging from ETL to models training and deployment.

Databricks deciphers the complexities of processing data to empower data scientists, engineers, and analysts with a simple collaborative environment to run interactive and scheduled data analysis workloads. The program takes advantage of AI’s cost-effectivity, flexibility, and cloud storage.

Databricks Key Features

Some of Databricks key features include:

• Cloud-native: Works well on any prominent cloud provider.
  
• Data storage: Stores a broad range of data, including structured, unstructured, and streaming.
  
• Self-governance: Built-in governance and security controls.
  
• Flexibility: Flexible for small-scale jobs as well as running large-scale jobs like Big Data processing because it’s built from Spark and is specifically optimized for Cloud environments.
  
• Data science tools: Production-ready data tooling, from engineering to BI, AI, and ML.
  
• Familiar languages: While Databricks is Spark-based, it allows commonly used programming languages like R, SQL, Scala, and Python to be used.
  
• Team sharing workspaces: Creates an environment that provides interactive workspaces for collaboration, which allow multiple members to collaborate for data model creation, machine learning, and data extraction.
  
• Data source: Performs limitless Big Data analytics by connecting to Cloud providers AWS, Azure, and Google, as well as on-premises SQL servers, JSON and CSV.

Reviews from Real Users

Databricks stands out from its competitors for several reasons. Two striking features are its collaborative ability and its ability to streamline multiple programming languages.

PeerSpot users take note of the advantages of these features. A Chief Research Officer in consumer goods writes, “We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for.”

A business intelligence coordinator in construction notes, “The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes.”

An Associate Manager who works in consultancy mentions, “The technology that allows us to write scripts within the solution is extremely beneficial. If I was, for example, able to script in SQL, R, Scala, Apache Spark, or Python, I would be able to use my knowledge to make a script in this solution. It is very user-friendly and you can also process the records and validation point of view. The ability to migrate from one environment to another is useful.”