Understanding “What is Databricks” is pivotal for professionals and organizations aiming to harness the power of data to drive informed decisions. In the rapidly evolving landscape of analytics and data management, Databricks has emerged as a transformative data platform, revolutionizing the way businesses handle data of all sizes and at every velocity. In this comprehensive guide, we delve into the nuances of Databricks, shedding light on its significance and its capabilities.
What is Databricks?
Databricks is a groundbreaking data warehousing, data engineering and data science platform, in that it is up to 12X faster than other platforms, and is the first completely unified, cloud-native data platform.
In asking “What is Databricks?”, it is clear that the company positions all of its capabilities within the broader context of its Databricks “Lakehouse” platform, touting it as the most unified, open and scalable of any data platform on the market. Built on open source and open standards, it creates a true, seamless “data estate” that combines the optimal elements of both data lakes and warehouses in order to reduce overall costs and deliver on data and AI initiatives more efficiently. It does this by eliminating the silos that historically separate and complicate data and AI and by providing industry leading data capabilities.
Powered by Apache Spark, a powerful open-source analytics engine, Databricks transcends traditional data platform boundaries. It acts as a catalyst, propelling data engineers, data scientists, a well as business analysts into unusually productive collaboration. In this innovative context, professionals from diverse backgrounds converge, seamlessly sharing their expertise and knowledge. The value that often emerges from this cross-discipline data collaboration is transformative.
The Key Components of Databricks
Databricks comprises several integral components, each playing a vital role in its functionality. At the highest level, the two main elements of Databricks are:
- The Databricks Workspace – as a collaborative environment where teams can create and manage notebooks, collaborate on code, and visualize data.
- The Databricks Runtime – the Databricks runtime provides optimized execution for Apache Spark jobs, ensuring high performance and reliability.
The main user capabilities in the platform that comprise the Databricks Lakehouse platform, in an overall context of seamless collaboration across the platform:
- Data Sharing – A secure, open, secure, zero-copy sharing of all data
- Data Governance – Unified governance of all platform objects including data, workflows, analytics and AI
- Data Management – Secured, performant and and reliable data and analytics
- Data Warehousing – Serverless cloud DW for analytics of every kind
- Data Engineering – Modern data orchestration/ETL for batch as as well as streaming data, and analytics and AI workflows.
- Data Streaming – Analytics, AI and streaming applications in real-time, made simple
- Data Science – Scalable, collaborative data science
- Artificial Intelligence – Create, train, and manage a full lifecycle of AI applications
- MosaicML – Train and deploy generative AI models in a secure, reproducible fashion
Additionally, Databricks Community Edition offers a free version of the platform, allowing users to explore its capabilities without initial financial commitments.
Understand the Databricks Architecture: Cloud Native
When understanding “what is Databricks”, questions around the Databricks architecture and deployment often arise. According the Databricks FAQ, while users can source data from on premise data sources of just about any kind, the platform itself is “currently available on Microsoft Azure, Amazon AWS and Google Cloud“.
Read our latest article on the Databricks architecture and cloud data platform functions to understand the platfrom architecture in much more detail.
Top 10 Databricks Benefits
- Up to 12X faster: Most importantly, customers consistently find that Databricks platform is much faster. Databricks claims it is up to 12X faster than the competitor platforms, with its newer Photon engine used for all workload types. This drives down compute costs (Databricks claims an 80% TCO increase) and can significantly reduces time-to-insight.
- Unified data platform: Databricks is the first true unified, collaborative platform for data engineers, data scientists, data analysts, and business analysts. The collaborative nature of all the capabilities leads to unique value being created across an organization’s data landscape.
- Cloud-native, multi-cloud: Databricks is a cloud-native solution that is currently available on AWS, Google and Azure.
- Started as a data engineering / data science pipeline platform: Unlike the competitors, Databricks started with data engineering and data science, and then moved into the much the simpler cloud data warehouse space. Competitors have been attempting to move from the cloud DW space into data engineering / datas science and are struggling, finding it much more complex. Highlights include:
- MLFLOW – The first true end-to-end machine learning lifecycle management capability (also an open source project). Also, with Databricks Time Travel, the platform uniquely solves for the problem of ML model results reproducibility which requires identical training and test data, logic, parameters, libraries, and environment.
- AuotML, MosaicML (GenA/LLMs), Model Serving, Feature Engineering/MLOps, AI Governance and more.
- Collaborative data / data science notebooks and LLM/Chat-based NLI for prompt-engineering.
- Hyperparameter model selection and tuning using Hyperopt python library.
- Powerful managed data orchestration service: Data orchestration workflows with full-featured data engineering / ETL capabilities including end-to-end monitoring, including all ML pipelines.
- Massively Scalable: With the Databricks delta lake at the core, the platform drives fresher data while maintaining ACID compliance, it also prevents data corruption, produces much faster queries and has built-in capabilities for common data compliance operations (for GDPR etc).
- Open Platform: Databricks supports most common frameworks, libraries and scripting languages including these examples:
- Frameworks: Spark MLlib, TensorFlow, Pytorch, Caffee2, sci-kit-learn, Keras and others
- Libraries: NVIDIA RAPIDS, Pypi, Maven, CRAN matplotlib, ggplot, pandas, NumPy and others
- Scripting languages: R, Python/Pandas, Scala, SQL (including UDFs
- IDEs/Repos: JupyterLab, RStudio, Visual Studio, PyCharm, IntelliJ IDEA, Eclipse / Github and Bitbucket
- Works seamlessly with well-known analytics platforms: Easy integration with common analytics platforms such as Tableau and PowerBI, while also having its own basic visualization layer.
- Streaming data as a first-class citizen of the platform: Databricks supports streaming ingestion and transformation, real-time analytics, real-time ML and real-time applications.
- First-of-its-kind unified monitoring: Monitoring across all workloads and data structures, including ML and streaming pipelines, leveraging its Unity Catalog.
Databricks Drives Business Value
Databricks drives significant and unique value for businesses aiming to harness the potential of their data. Its ability to process and analyze vast datasets in real-time equips organizations with the agility needed to respond swiftly to market trends and customer demands. By incorporating machine learning models directly into their analytics pipelines, businesses can make predictions and recommendations, enabling personalized customer experiences and driving customer satisfaction. Furthermore, Databricks’ collaborative capabilities foster interdisciplinary teamwork, fostering a culture of innovation and problem-solving.
Conclusion: A Unified Data Future
Understanding “What is Databricks” is essential for businesses striving to stay ahead in the competitive landscape. Its unified data platform, collaborative environment, and AI/ML capabilities position it as a cornerstone in the world of data analytics. By embracing Databricks, organizations can harness the power of data and data science, derive actionable insights, and drive innovation- propelling them forward. When considering how to discover how Databricks would best support your business, check out our AI consulting guidebook to stay ahead of the curve and unlock the full potential of your data with Databricks.
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