5 Ways to Become a Data Driven Organization With Databricks

6 min readJul 14, 2022

In the post-pandemic world of rapid digitalization, more and more companies are pursuing the goal of becoming data driven organizations. Making better use of data and the insights it provides is critical for sustainable growth and scalable business development. Platforms such as Databricks help enterprise organizations harness the power of their data while reducing cost, facilitating processes, and allowing for overall better data management.

When we launched our Data Center of Excellence (DCOE) earlier this year, we also partnered with Databricks as our preferred data management vendor. We chose them because their platform is best-suited to the types of data projects we spin up for our clients looking for data management services. Specifically, here are five ways that Databricks can help your organization become more data-driven.

#1. Databricks keeps all your data together

The Databricks platform is an established leader in data lake and warehousing technology. They combined these two concepts into a data lakehouse architecture that allows organizations to benefit from both while solving most drawbacks. Delta Lake on Databricks is an open-source storage layer that brings reliability to data lakes. It provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Best of all, it runs on top of your existing data lake and is fully compatible with Apache Spark APIs.

Delta Lake on Databricks allows you to configure Delta Lake based on your workload patterns. With these few essential features, it brings all classical data warehouse benefits to the data lake while not having the storage and processing limitation of a monolith data warehouse. This makes the Databricks platform fully capable of handling all your organization’s data in a single place.

#2. Databricks has the best engine for processing data in different languages

Databricks uses Apache Spark, an open-source cluster computing solution and in-memory processing framework, as its processing engine. It extends the MapReduce model to support other computations such as interactive queries and stream processing. Engineered from the ground up for performance, Spark is 100x faster than Hadoop for large-scale data processing because it exploits in-memory computing and other optimizations.

Spark is also fast when data is stored on disk and currently holds the world record for large-scale on-disk sorting. Moreover, it has easy-to-use APIs for operating on large datasets. This includes a collection of over 100 operators for transforming data and familiar data frame APIs for manipulating semi-structured data. The Databricks notebooks allow using the most common data manipulation languages, including SQL, Python, Java, R, and Scala.

#3. Databricks makes infrastructure management and deployments easy

Databricks provides your team with access to readily available and performance-optimized Spark clusters in a self-service fashion. That wide availability allows everyone to build and deploy advanced analytics applications without DevOps expertise. With Databricks, you always have access to the latest Spark features — leverage the latest innovation from the open-source community and focus on your core mission instead of managing the infrastructure. Databricks also offers monitoring and recovery mechanisms that automatically recover clusters from failures without manual intervention.

With Databricks, your infrastructure is fast and secure without any custom work in Spark.

Databricks takes an API-first approach to building features on the platform. With each feature, the APIs are built first before a UI is developed. With the Databricks CI/CD Integrations, you can streamline your application development process with integrations to DevOps tools such as Git, Jenkins, Terraform, CircleCI, and Azure DevOps. It also allows you to streamline operations with monitoring, auto-scaling, VM pools, and multiple regions support.

#4. Databricks offers seamless collaboration

The key here is the Databricks notebooks. These notebooks have many features never before integrated in such a manner.

  • Data access: Quickly access available data sets or connect to any data source, on-premises or in the cloud.
  • Multi-language support: Explore data using interactive notebooks with support for multiple programming languages within the same notebook, including R, Python, Scala, and SQL.
  • Interactive visualizations: Visualize insights through a wide assortment of point-and-click visualizations, or use powerful scriptable options like Matplotlib, ggplot, and D3.
  • Real-time co-authoring: Work on the same notebook in real-time while tracking changes with detailed revision history.
  • Automatic versioning: Automatic change-tracking and versioning help you pick up where you left off.
  • Git-based repos: Simplified Git-based collaboration, reproducibility, and CI/CD workflows.
  • Runs sidebar: Automatically log experiments, parameters, and results from notebooks directly to MLflow as runs, and quickly see and load previous runs and code versions from the sidebar.
  • Dashboards: Share insights with your colleagues and customers, or let them run interactive queries with Spark-powered dashboards.
  • Run notebooks as jobs: Turn notebooks or JARs into resilient production jobs with a click or an API call.
  • Jobs scheduler: Execute jobs for production pipelines on a specific schedule.
  • Notifications and logs: Set alerts and quickly access audit logs for easy monitoring and troubleshooting.

Integrating all these features into a single notebook provides a never-before-seen boost in data engineering and science productivity. At the same time, it increases collaboration using the organization’s data.

#5. Databricks facilitates next-level integrations

Databricks integrates with a wide range of data sources, developer tools, and partner solutions. This allows data scientists and engineers to use their tools of choice while still using all advantages of the Databricks platform.

  • Data sources: Databricks can read data from and write data to a variety of data formats such as CSV, Delta Lake, JSON, Parquet, XML, and other formats, as well as data storage providers such as Amazon S3, Google BigQuery, and Cloud Storage, Snowflake, and other providers.
  • Developer tools: Databricks supports various developer tools such as DataGrip, IntelliJ, PyCharm, Visual Studio Code, and others that allow you to work with data through Databricks clusters and Databricks SQL warehouses by writing code.
  • Partner solutions: Databricks has validated integrations with third-party solutions such as Fivetran, Power BI, Tableau, and others. They allow you to work with data through Databricks clusters and SQL warehouses, with low-code and no-code experience in many cases. These solutions enable common scenarios such as data ingestion, data preparation and transformation, business intelligence (BI), and machine learning. Databricks also provides Partner Connect — a user interface that allows some of these validated solutions to integrate more quickly and easily with your Databricks clusters and SQL warehouses.


Databricks is a fully-managed platform pushing the boundaries of data processing, infrastructure management, data science, and data engineering collaboration. It also easily integrates with various tools that allow any organization to become data-driven. Databricks improves the speed and capabilities of that journey and is a reliable partner for managing the organization’s most important assets — data and information.

Original post found here.

Authored by Boyan Stoyanov:

Boyan is a highly-qualified data engineering professional with a rich background in data management. He’s worked for some of the biggest organizations in various industries such as finance, gaming, betting, and science.

He joined MentorMate in 2021 as a Senior Data Engineer and played a major role in the creation of our Data Center of Excellence. As one of the key figures in our data team, Boyan applies and develops his expertise in data engineering and automation, Business Intelligence, SQL, and Database performance tuning.

Boyan is interested in developing and implementing solutions utilizing the cloud services AWS and Azure, data processing and management using Python, pySpark, and Databricks, as well as Data Science and predictive analytics.

He holds various certifications such as AWS Certified Data Analytics Specialty, AZ-900 Exam-Prep: Microsoft Azure Fundamentals, Python (Basic) — Hackerank, Oracle Database 11g: Administration I — 1z0–052, Oracle Database SQL Expert — 1Z0–047, and Oracle Database 11g Performance Tuning Certified Expert — 1Z0–054.

He loves to spend his free time with his kid, playing volleyball, billiard, DOTA 2, and learning new technologies.




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