Data-driven decision-making has become the foundation of business operations across every type of company, no matter the size or industry. Large volumes of data flow from many source systems to data warehousing, data lake, or analytics solutions.
What companies need to maximize their ROI from data is a fast, dependable, scalable, and user-friendly space that brings all kinds of data practitioners together, from data engineers and analysts to ML folks.
This is where Databricks comes in.
Databricks is a cloud-based data engineering tool teams use to analyze, manipulate, and study massive amounts of data. It’s an essential tool for machine learning teams that helps to analyze and convert large volumes of data before exploring it with machine learning models. It enables businesses to swiftly realize the full potential of their data, be it via ETL processes or cutting-edge machine learning applications.
What is Databricks and how can it help your team?
This article dives into Databricks to show you what it is, how it works, its core features and architecture, and how to get started.
What is Databricks?
Databricks is a cloud-based platform that serves as a one-stop shop for all data needs, such as storage and analysis. It was created by the people behind Apache Spark. Databricks can generate insights with SparkSQL, link to visualization tools like Power BI, Qlikview, and Tableau, and develop predictive models with SparkML. You can also use Databricks to generate tangible interactive displays, text, and code. One could say it’s a MapReduce system alternative.
Databricks deciphers the complexity of data processing for data scientists and engineers, allowing them to build machine learning applications in Apache Spark using R, Scala, Python, or SQL interfaces.
Another important point is the platform’s integration with the cloud ecosystem. Databricks is well-integrated with the three major cloud service providers: Microsoft Azure, Amazon Web Services, and Google Cloud Platform. This lets enterprises handle massive amounts of data and perform machine learning operations with ease.
What problem does Databricks solve?
What is Databricks all about? Organizations collect large volumes of data in data warehousing or data lakes. Data is frequently exchanged between them at a high frequency – a process that often turns out to be complex, costly, and non-collaborative.
Databricks solves this problem by simplifying big data analytics via the lakehouse architecture, which gives a data lake the capabilities of a data warehouse. As a result, it removes data silos that often emerge when data is pushed into a data lake or warehouse. That way, the lakehouse architecture offers data teams a single source of data.
What is Databricks used for?
Many businesses now use a rather complicated combination of data lakes and data warehouses, with parallel data pipelines that handle data that arrives in planned batches or real-time streams. Next, they typically add an array of other analytics, business intelligence, and data science tools on top.
Databricks makes this approach obsolete because it has everything data practitioners need under one roof: a lakehouse architecture.
Some common use cases of Databricks include:
- Collecting all of your data in one location
- Handling both batch data and real-time data streams
- Transforming and organizing data
- Performing computations on data
- Querying data
- Analyzing data and generating reports to support business decision-making
Naturally, you can also combine Databricks with other technologies inside your data ecosystem. Many teams get started with Databricks this way to understand what the platform is capable of and how it can help them solve the most pressing data-related challenges.
Who uses Databricks?
Large organizations, small businesses, and everyone in between uses the Databricks platform today. International brands like Coles, Shell, Microsoft, Atlassian, Apple, Disney, and HSBC use Databricks to handle their data demands swiftly and efficiently.
Because of the breadth and performance of Databricks, it csn be used by all members of a data team, including data engineers, data analysts, business intelligence practitioners, data scientists, and machine learning engineers.
Databricks vs. database vs. data warehouse
Before we move forward in exploring Databricks, it’s important to clarify the differences between databases, data warehouses, lakes, and Databricks, which proposes a brand-new architecture: data lakehouse.
A database is a tool that collects data and is often used to enable Online Transaction Processing (OLTP). Database Management Systems (DBMS) are types of software that store data in databases and allow users and applications to interact with the data.
A data warehouse is a system that collects and maintains highly organized data from numerous sources. Typically, traditional cloud data warehouses contains both current and historical data from one or more systems. You can use a data warehouse to consolidate diverse data sources in order to analyze the data, search for insights, and provide business information (BI) in the form of reports and dashboards.
A data lake is a collection of data from several sources kept in its original, unprocessed form. Like data warehouses, lakes hold massive volumes of current and historical data. Data lakes are distinguished by their capacity to store data in a number of forms, such as JSON, BSON, CSV, TSV, Avro, ORC, and Parquet.
The primary goal of a data lake is often to analyze data to deliver insights. However, teams will occasionally employ lakes for cheap storage with the intention of using the data for analytics in the future.
A data lakehouse is a new type of open data management architecture that combines the scalability, flexibility, and low cost of a data lake with the data management and ACID transactions of data warehouses. This lets you do BI and ML on any data.
A Databricks lakehouse integrates identical data structures and data management functions to those in a data warehouse, directly on the type of low-cost storage used for data lakes. By merging these two approaches into a single system, data teams can work faster since they can find all the data they need in one place. Data lakehouses also guarantee that teams have access to the most current and complete data for data science, machine learning, and business analytics initiatives.
Key features of Databricks
Databricks links to a large number of data sources – not just to AWS, Azure, or Google Cloud storage services, but also to on-premise SQL servers, CSV, and JSON. The platform also supports MongoDB, Avro files, and a variety of other file types.
Databricks was developed on top of Apache Spark, which has been especially tuned for cloud-based deployments. In the data science scene, Databricks provides scalable Spark tasks, handling small-scale tasks like development or testing as well as large-scale tasks like processing data.
Databricks has a notebook interface that allows you to use several coding languages in the same environment. You can create algorithms in Python, R, Scala, or SQL by using simple commands. Data transformation operations can be carried out using Spark SQL, model predictions produced with Scala, model performance assessed with Python, and data displayed with R.
Databricks boosts productivity by allowing users to rapidly deploy notebooks into production. The platform fosters collaboration since it provides a shared workspace for data scientists, engineers, and business data analysts.
Enhanced collaboration not only generates new ideas but also helps others to implement frequent adjustments while also speeding up development processes. Databricks maintains recent changes with an integrated version control tool, which decreases the work required to locate recent changes.
Benefits of Databricks
Databricks was developed with cloud-based deployments in mind. It can handle a wide range of cloud-native scenarios. Databricks actually uses Kubernetes to coordinate containerized workloads for product microservices and data-processing processes.
Databricks stores data files and tables in the cloud using object storage. It configures a cloud object storage location known as the DBFS root during workspace deployment. In your account, you can also configure connections to various cloud object storage sites.
Data lakes are open format allowing users to avoid lock-in to a proprietary system such as a data warehouse. Because of their capacity to grow and exploit object storage, a data lake is also extremely long-lasting and low-cost. Databricks include the capabilities of a data lake, the top choice for data storage because of its unique capacity to absorb raw data in a number of formats (formatted, unstructured, semi-structured).
Governance and Management
With features such as the Databricks Unity Catalog and Delta Sharing, Databricks delivers unified governance for data. Using Unity Catalog, you can centralize access control and with Delta Sharing, share data. Other capabilities include audit tracking, IAM, and solutions for legacy data governance.
Data Science Tools
The Databricks platform is used to process, store, clean, distribute, analyze, model, and monetize data using solutions ranging from data science to business intelligence. Datbricks supports a wide range of data science use cases.
What Data Areas Can Databricks Support?
Built on Apache Spark, Azure Databricks enables data engineers and data analysts to deploy data engineering workflows and perform Spark jobs to process, analyze, and display data at scale. Delta Lake is an open source relational storage area for Spark that you can use with Databricks to build a data lakehouse architecture.
Data engineers can also use the Databricks Lakehouse Platform to:
- Handle key data pipeline development tasks
- Create production data pipelines with SQL and Python to extract, manipulate, and load data into lakehouse tables and views
- Use Databricks-native capabilities and terminology, such as Delta Live Tables, to simplify data import and incremental change propagation
- Deliver new findings for ad-hoc analytics and dashboarding by orchestrating production workflows
Databricks SQL (DB SQL) is a serverless data warehouse built on the Databricks lakehouse platform that lets users run all of their SQL and BI applications at scale with optimal performance, consistent data governance architecture, open formats and APIs, and preferred tools with no lock-in. With serverless, there’s no need to maintain, install, or grow a cloud infrastructure.
Databricks Machine Learning and Data Science
Data scientists and machine learning engineers also stand to benefit from the open lakehouse architecture. It enables ML teams to prepare and analyze data, accelerates cross-team communication, and standardizes the whole ML lifecycle from testing to production, including generative AI and huge language models.
Managed MLflow records all the experiments and logs parameters, metrics, data and code versioning, and model artifacts with each training run. You can quickly check prior runs, compare findings, and duplicate a previous result. Once you’ve determined the optimum version of a model for production, add it to the Model Registry to streamline handoffs throughout the deployment lifecycle.
Databricks Use Cases
The Databricks platform provides a uniform interface and tools for the majority of data jobs, such as:
- Data processing workflows scheduling and management for data processing
- Using SQL
- Batch data processing
- Dashboard and visualization creation
- Data ingestion
- Discovering, annotating, and exploring data
- Modeling and tracking using machine learning (ML)
- Managing security, data governance (unified data governance model), and disaster recovery
Databricks was designed to deliver a safe cross-functional team collaboration platform while also managing a considerable number of backend services to let team focus on data science, data analytics, and data engineering tasks.
Databricks operates on two planes: the control plane and the data plane.
The control plane includes the backend services that Databricks operates on its own AWS account. Notebook commands and many other workspace parameters are encrypted at rest and kept on the control plane as well.
The data plane is where your data gets processed. Note that your data lake is kept in your own cloud account at rest. Job results are saved to your account as well.
Learn more about Databricks architecture.
Getting Started With Databricks
You can start with a free trial of Databricks to take the first step and check out how the platform works. To do that, you can head over to the Databricks website. If you’re an AWS user, you’ll also find Databricks on the AWS Marketplace.
Next, you can pick your subscription package and create your first Databricks workspace. And then take Databricks for a spin to see what it can do for you!
Databricks documentation is extensive and provides a wealth of resources to help you get started. Check out this Getting started page.
Databricks has the ability to provide a single platform for data processing, analytics, and machine learning – this is its primary competitive advantage: to unify enterprise data solutions. By implementing Databricks, data teams no longer need to invest in many other tools, reducing complexity and simplifying the analytics process.
Thanks to its emphasis on performance improvement, speed capabilities, a wide range of sophisticated analytics and machine learning technologies, and a collaborative environment for data professionals, Databricks is a key platform in modern data ecosystems.
Databricks includes some version control capabilities, but if you’d like to extend them, you can easily integrate an open-source tool like lakeFS.
lakeFS lets you manage your data as code using Git-like operations (branching, merging, committing, etc.) to achieve reproducible, high-quality data pipelines.
Here’s a step by step tutorial for configuring lakeFS on Databricks to show you how easy it is to bring the two solutions together and enjoy full data version control capabilities: Databricks and lakeFS Integration: Step-by-Step Configuration Tutorial.
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