Data platform
Databricks at a glance: Answers to the 25 most important questions
Ailio
Databricks is one of the leading platforms in the field of big data, analytics and AI. It is incredibly powerful, but many questions can arise, especially for beginners or during evaluation. What exactly is the Lakehouse? How does pricing work? What is Databricks best for?
To shed some light on the matter, here at Ailio we have collected the 25 most frequently asked questions about Databricks and answered them briefly and understandably for you. This FAQ article serves as a quick introduction and reference work.
(Optional: If you have more in-depth questions or need support with your Databricks project, Ailio's experts are happy to help!)
Top 25 Databricks Questions:
- What is Databricks? Databricks is a cloud-based unified analytics platform built on Apache Spark. It combines data engineering, data science, machine learning and business analytics on one platform to simplify the entire data lifecycle.
- What is the Lakehouse concept? The Lakehouse is a modern data architecture concept popularized by Databricks. It combines the flexibility and cost-effectiveness of a data lake (storage of all data types) with the reliability, performance and management functions (e.g. ACID transactions) of a data warehouse.
- What is Delta Lake? Delta Lake is an open source storage format that is at the heart of Databricks Lakehouse. It extends data lakes (such as those on Azure ADLS or AWS S3) with ACID transactions, data versioning (time travel), schema enforcement, and improved performance for big data workloads.
- Is Databricks just Apache Spark? No. Although Databricks is based on Apache Spark, it offers much more: an optimized Spark engine (Photon), a collaborative development environment (notebooks), management tools (clusters, jobs), integrated ML tools (MLflow), governance functions (Unity Catalog) and a user-friendly interface - all as a managed service in the cloud.
- What cloud platforms does Databricks run on? Databricks is a multi-cloud platform and runs on the three major hyperscalers: Microsoft Azure (as Azure Databricks), Amazon Web Services (AWS), and Google Cloud Platform (GCP).
- What is Azure Databricks? Azure Databricks is a Databricks offering integrated as a first-party service with Microsoft Azure. It is deeply integrated into the Azure ecosystem (e.g. Azure Active Directory, ADLS Gen2, Azure ML, Power BI) and is managed and billed directly via the Azure portal.
- What are the key benefits of Databricks? Unification (one platform for all data workloads), scalability (processing massive amounts of data), performance (optimized Spark engine), openness (based on open source), collaboration (common working environment), and strong AI/ML integration.
- What use cases is Databricks suitable for? Large-scale ETL/ELT and data engineering, lakehouse data warehousing, exploratory data analysis, machine learning model development and operation, real-time streaming analytics, and interactive SQL analytics/BI.
- What are Databricks Notebooks? Notebooks are web-based, interactive documents in which users can combine code (Python, SQL, Scala, R), explanatory text, visualizations, and mathematical formulas. They are the central tool for development and exploration in Databricks.
- What programming languages does Databricks support? The main languages are Python, SQL, Scala and R. They can often even be mixed within the same notebook.
- What are Databricks Clusters? Clusters are groups of cloud VMs (virtual machines) that provide the computing power for Databricks workloads. They consist of a driver node and multiple worker nodes and can be configured and scaled (manually or automatically) as needed.
- How does Databricks pricing work? (DBUs) Databricks is billed primarily via Databricks Units (DBUs). DBUs are a normalized measure of the computing power consumed per second while a cluster is running. The cost per DBU varies depending on the cloud provider, region, VM type and workload type (e.g. Jobs Compute vs. All-Purpose Compute). There are also costs for the underlying cloud infrastructure (VMs, storage, etc.).
- How to optimize Databricks costs? By right-sizing clusters, using autoscaling and auto-termination, using cheaper job clusters for automated tasks, using spot instances, code optimization and using cluster policies to control costs.
- What is Databricks SQL? Databricks SQL (DBSQL) provides a dedicated user interface and optimized compute resources (SQL warehouses) for SQL analysts to perform BI workloads and SQL queries directly on the data in the lakehouse.
- How does Databricks integrate with BI tools like Power BI or Tableau? Very good. BI tools can connect directly to the data in the Databricks Lakehouse via optimized connectors (often via Databricks SQL Warehouse) to create dashboards and reports.
- What is MLflow? MLflow is an integrated open source platform within Databricks (and usable outside) for managing the entire machine learning lifecycle. It includes experiment tracking, code packaging, model registry and deployment functions.
- What is Photon? Photon is a C++-based vector execution engine developed by Databricks that is compatible with Apache Spark APIs. It significantly accelerates many SQL and DataFrame operations and can thus increase performance and reduce DBU costs.
- What is Unity Catalog? Unity Catalog is Databricks' central governance solution for data and AI assets in the lakehouse. It offers fine-grained access control, a data catalog, data lineage, and sharing capabilities across different workspaces (and potentially clouds).
- How secure is Databricks? Databricks offers robust, enterprise-grade security features including network isolation (VNet injection), encryption (at rest, in transit), role-based access control (RBAC), integration with identity providers (like Azure AD), and comprehensive audit logs. However, security also depends on the correct configuration and security of the underlying cloud environment.
- How is Databricks different from Snowflake? Both are strong cloud data platforms. Snowflake is primarily a cloud data warehouse with a strong focus on SQL and ease of use. Databricks positions itself as a unified platform for data warehousing, data engineering and data science/ML based on the open lakehouse concept with strong Spark integration.
- How is Databricks different from Azure Synapse Analytics? Azure Synapse is Microsoft's integrated analytics platform that combines DWH, Spark and data integration. Azure Databricks is the specialized, Spark-based lakehouse service on Azure. Often they are combined: Synapse for SQL DWH parts, Databricks for complex Spark jobs and ML, or Databricks as the sole lakehouse engine on ADLS with Synapse only as a frontend/SQL endpoint.
- What are Delta Live Tables (DLT)? DLT is a framework within Databricks that simplifies the development and management of reliable ETL pipelines. It enables the declarative definition of data flows, automates the management of the infrastructure and integrates data quality checks.
- Can Databricks run on-premise? No, Databricks is a pure cloud platform and runs on AWS, Azure or GCP.
- What is the best way to learn Databricks? Through the official Databricks documentation, the Databricks Academy (training & certifications), online courses (Coursera, Udemy etc.), hands-on experiments with the Community Edition or a Cloud Trial and by working with experienced partners.
- How do I get started with Databricks? Choose your preferred cloud provider (Azure, AWS, GCP), create a Databricks workspace through their portal, configure an initial cluster, and start exploring the notebooks and tutorials.
Conclusion
We hope this quick Q&A helped you get a clearer picture of Databricks. The platform is incredibly versatile and a powerful tool for any data-driven company.
Of course, this FAQ only scratches the surface. If you have specific questions about your project, would like detailed advice or need support in implementing and optimizing your Databricks environment, Ailio is your experienced contact.
Feel free to contact us for a non-binding discussion!