What is Databricks? Functionalities and Importance
Data and data pipelines are the focus of businesses that drive their decisions through business intelligence. And to ensure the best use of data, businesses invest in different tools for data-related processes such as integration, processing, analysis, etc.
But managing this variety of data tools and maintaining efficiency becomes difficult, especially when companies want to scale up. The solution? A single tool that takes care of all data and business intelligence requirements.
Databricks is one such tool that offers complete, data warehouse solutions. It is a powerful data storage and analytics platform that can integrate with a number of BI tools and advanced analytical technologies such as AI.
Developed by the creators of Apache Spark, Databricks uses machine learning and distributed processing to speed up ETL processes - something that traditional data warehouses cannot do. This is useful for businesses that routinely process large volumes of data or continually rely on analytics.
An online electronics store, for instance, can use customer behavioural data to give personalized recommendations. If they use conventional data warehouses, processing will take hours or even days, delaying the recommendations.
On the other hand, Databricks processes data at short intervals delivering real-time recommendations. This means better personalization, customer experience, and more sales.
You can do much more by understanding how Databricks functions. In this article, we explain its features, architecture, and use cases. But first, let's understand what Databricks is.
What is DataBricks?
Databricks is a cloud-based platform that offers big data and machine learning services. It was founded by the creators of Apache Spark, a popular open-source big data processing framework, and provides a collaborative environment for data processing and machine learning. The platform integrates with cloud services such as AWS, Microsoft Azure, and Google Cloud, enabling organizations to leverage the advantages of the cloud, such as scalability, security, and lower costs.
Databricks provides an interactive notebook environment for data engineers and scientists to collaborate on data processing and machine learning tasks. It supports programming languages like Python, Scala, and SQL and integrates with big data technologies like Apache Hive, Apache Spark, and Apache Kafka.
The platform also offers advanced analytics capabilities such as graph processing, time-series analysis, and streaming data processing.
Databricks provides a cost-effective solution for big data processing and machine learning compared to building and maintaining an in-house infrastructure. It offers a pay-per-use pricing model that enables organizations to only pay for the resources they use. The platform also provides enterprise-grade security features such as role-based access controls, data encryption, and secure data sharing, ensuring that sensitive data is protected and meets compliance requirements.
Features of Databricks
Databricks is a fast and reliable ETL tool that speeds up data processing by up to 10 times. It also has other features that make Databricks an excellent data processing platform for your business:
Interactive Notebooks
Databricks has an interactive notebook interface that can be used to write codes, comments, and explanations. The interface supports various programming languages like SQL, Python, and Scala. The notebooks help you easily manage project development by keeping track of code alterations and execution.
Collaboration
Databricks has a shared workspace with collaboration tools to manage notebooks among multiple users. You can share the notebooks among team members, enable suggestions, or even iterate them with Git to manage code changes.
Collaboration tools such as comments and feedback are useful in taking recommendations from multiple users for solving complex code, implementing data processes, and so on.
Cloud Optimization
Databricks has a foundation of Apache Spark, which is highly suitable for a cloud interface. Through Apache Spark, Databricks can scale for any volume of data processing.
For example, if there is a high demand for data processing at the start of the ETL process, the interface automatically scales clusters to quickly analyze all data. As the demand falls, the compute clusters also scale down to accommodate the fluctuating data levels.
The platform also manages pre-provisioned instances to reduce the time and capacity required for any data process in the cloud interface.
Data Analytics
With Databricks, you can connect to any cloud or on-premise data source to process large data volumes. It provides various analytics tools, including SQL processing, machine learning, integration, and visualization, to develop accurate insights. These tools can handle any number of queries in a short time to produce real-time results.
Databricks Architecture
Databricks combines four open-source cloud tools in a data lake house architecture. Unlike data warehouses or data lakes, lake houses have advanced capabilities to run machine learning queries, simplifying ETL and business analytics.
The platform can be connected with existing cloud storage platforms such as AWS or Google Cloud and placed on top of data warehouses. This data source supplies data to the other layers of the Databricks platform.
Layers of Databricks Architecture
Databricks has an integrated user interface of four layers:
Data Lake
The data lake is the first storage layer that integrates all structured, unstructured, and semi-structured data. This can contain data from all pipelines, including CRMs, ERPs, social media platforms, emails, and company resources. The data in data lakes isn't organized and is stored in its native form.
Delta Lake
Delta Lake is an advanced storage layer that adds structure and reliability to the data lake. The layer adds a schema to the data lake to organize the data for analysis. It also ensures that all transactions in the data lake are atomic, consistent, isolated, and durable (ACID transactions).
With Delta lake, you get features such as schema evolution, time traveling, and versioning that are unavailable in traditional data lakes. You can also use it for large volumes of data processing due to its Apache Spark build.
Delta Engine
Delta engine is a query processing engine that runs large-scale queries on data stored in Delta lake. The engine uses a distributed framework to automatically optimize query processing depending on the queries' volume, size, and complexity.
This improves Delta lake workload efficiencies and allows for large-scale batch data processing. Delta engine also provides automatic indexing and caching to improve Delta lake performances.
Other Tools
After query processing, the data can be used for various analytics and business intelligence processes. Databricks provides built-in tools for almost every BI use case, including machine learning, visualization, and notebooks. Moreover, it is also integrable with other analytics tools such as Redash, MLFlow, and Power BI.
The four-layer architecture of Databricks is optimal for complex or large-scale data processing and machine learning requirements. You can further utilize Databricks for your business by learning about the importance of the platform.
Importance
Scalability
Databricks provides a scalable platform for processing big data and running machine learning workloads. It integrates with cloud services such as AWS, Microsoft Azure, and Google Cloud for seamless scaling.
Ease of Use
Databricks provides an intuitive, user-friendly interface for data processing and machine learning tasks. This makes it easier for data scientists and engineers to focus on the core aspects of their data processing workflows scheduling without worrying about the underlying infrastructure.
Integration
Databricks integrates with popular big data technologies such as Apache Hive, Apache Spark, and Apache Kafka for seamless data processing and machine learning.
Speed
Databricks offers fast processing speeds for big data and machine learning tasks. This enables organizations to gain insights from their data quickly and make data-driven decisions.
Cost Effective
Databricks provides a cost-effective solution for big data processing and machine learning compared to building and maintaining an in-house infrastructure. It also provides a pay-per-use pricing model that enables organizations to only pay for the resources they use.
Advanced Analytics
Databricks provides advanced analytics capabilities such as graph and processing data mine, time-series analysis, and streaming data processing that enable organizations to gain deeper insights from their data.
Cloud Integration
Databricks integrates with popular cloud services such as AWS, Microsoft Azure, and Google Cloud. This enables organizations to leverage the advantages of the same cloud infrastructure, such as scalability, security, and lower costs.
Enterprise-Grade Security
Databricks provides security features such unified data governance model such as role-based access controls, data encryption, and secure data sharing. This ensures that sensitive data is protected and meets compliance requirements.
Easy Deployment
Databricks enables easy deployment of machine learning models through its platform. This makes it easier for organizations to operationalize their machine-learning models and gain real-time insights from their data.
Community Support
Databricks is backed by a large and active community of developers and data scientists. This gives organizations a wealth of knowledge and resources for big data processing and machine learning.
Setting Up Databricks
You can set up Databricks on any major cloud platform, such as GCP, AWS, and Azure. The platform offers a 14-day free trial on three types of paid subscriptions: Standard, Premium, and Enterprise. Depending on the data processing requirements, you can choose any package and proceed with the trial.
Here, we will show you how to set up Databricks on AWS.
Step 1
Visit the Databricks website, sign up, and choose Amazon Web Services as your cloud platform.
Step 2
Setup your Databricks account by verifying your email and choosing a subscription plan. In the next page, enter a workspace name and password.
Step 3
Create a workspace by entering your workspace name and choosing an AWS region. Ideally, the region should be close to the location of your business.
Then, click on “Start Quick Start” to open the AWS Quick Start Form. In the Quick Start form, navigate to the “Quick Create Stack” tab and enter your workspace password, check the required boxes, and click “Create Stack.”
This opens the “databricks-workspace-stack” page.
Step 4
The “databricks-workspace-stack” page helps you track your workspaces. When the new workspace creation is started, you will see the status as “CREATE_IN_PROGRESS.”
After the workspace creation is complete, the status changes to “CREATE_COMPLETE.”
Navigate back to the Databricks page to see the new workspace. You can also click open to launch it in a new tab.
Step 5
Now, you can use the workspace for data analytics and business intelligence processes. For example, to run a query, first create a cluster through the “Compute” tab located in the sidebar.
Next, create a notebook by navigating to Workspace>Create>Notebook. Then, create a table for CSV data into the Delta lake format.
Step 6
Analyze the data by running a query on the table using SQL statements. You can display the results by clicking on the “Bar Chart” options on the side menu.
You can also use Databricks to create jobs, manage clusters, load data, and share data through Delta sharing.
Benefits of Databricks
Databricks offer greater data management and analytical capabilities compared to traditional data warehouses. Apart from this, Databricks has several other benefits, such as:
Framework Support
Databricks supports popular ML and deep learning frameworks, including TensorFlow and Keras. It also supports related libraries such as pandas and matplotlib, making data processing, analysis, and visualization easier.
Unified Data Analytics Platform
Through Databricks, all data science-related teams can collaborate using the same platform. Databricks allows shared notebooks and gives access to comments, feedback, and other collaboration tools. This helps data engineers, analysts, and business intelligence teams simultaneously work on the same data queries.
Flexibility
Databricks supports all major cloud storage platforms, such as GCP, AWS, and Azure. You can integrate your existing cloud platform with Databricks to get a seamless data processing experience without loading the data onto a new platform.
Scalability
Databricks runs on Delta lake, a highly scalable object storage solution. Delta lake is built using cloud storage options such as Amazon S3 and Google Cloud Storage, allowing it to scale automatically depending on the data requirements.
Built-in Tools
Databricks facilitates all data analytics processes on a single platform using built-in tools. Runtime and Delta Sharing help businesses with distributed data systems run machine learning and visualization processes without requiring third-party applications.
MLFlow Integration
Databricks integrates with MLFlow to simplify machine learning queries on the data in Delta lakes. MLFlow helps build and manage ML-related data queries with Auto ML and model lifecycle management.
Hyperopt Integration
Databricks also integrates with Hyperopt to optimize data queries. Hyperopt is an open-sourced library for machine learning models that helps with hyperparameter tuning for specific metrics.
Source Code Management
Databricks helps you manage, alter, and control source code using tools like Github and Bitbucket. Through the tools, Databricks allows you to clone repositories, push and pull changes, and collaborate with other users for source code management.
Faster ETLs
Databricks uses a distributed processing model that breaks down large queries into smaller parts for simultaneous processing. This model returns queries 10x faster than ETL processes using traditional data warehouses.
Role-based Databricks adoption
Databricks is designed for all kinds of data processing and analytics use cases. As a result, different roles within a team can utilize Databricks in unique ways. Here, we explain these role-based use cases so companies can adopt Databricks more efficiently.
Data Analyst
Data analysts can use built-in visualization tools to create graphs, charts, and dashboards to generate insights easily understandable by stakeholders. They can also share and modify these visualizations with other members through shared notebooks and collaboration workspaces.
Data Scientist
Data scientists are responsible for the data governance and collecting and analyzing data. They can leverage Databricks using its advanced data processing capabilities, integrated libraries, and machine learning capabilities through integrated tools such as MLFlow.
Data Engineer
Data engineers can improve data quality and scale pipelines using Databricks. The platform enables indexing, partitioning, and caching to optimize data pipelines. It also connects with major data sources and helps manage data pipelines through Databricks jobs.
Machine Learning Practitioner
Databricks provides various machine-learning tools and libraries to train data models. Machine learning practitioners can use TensorFlow, PyTorch, and other libraries and train machine learning models through MLFlow and Hyperopt.
Other than these, Databricks can also be used by business intelligence, cybersecurity, and IT professionals for large-scale data processing and machine learning requirements.
For businesses with data requirements beyond extraction and loading, Databricks is the perfect tool for all data processes. It offers solutions unavailable in traditional data warehouses or even data lakes.
The databricks data intelligence platform that's advanced machine learning and analytical capabilities are useful for any organization with intelligent decision-making insights. Moreover, Databricks has several use cases for every data-related role in an organization.
In this article, we explained in-depth Databricks, its features, architecture, advantages, and uses. We also gave a step-by-step guide for setting up Databricks using the Amazon Web Service platform.
While Databricks can be a powerful data platform for any business, it is specifically designed for large-scale data processing. Before adopting the platform, you need to analyze your business requirements regarding data volume, scale, technical expertise, and budget.
This will also help you choose the right Databricks subscription plan that fits your business goals.
FAQs
What format does Delta Lake use to store data?
Delta Lake uses versioned Parquet files to store your data in your cloud storage. Apart from the versions, Delta Lake also stores a transaction log to keep track of all the commits made to the table or blob store directory to provide ACID transactions.
Where does Delta Lake store the data?
When writing data, you can specify the location in your cloud storage. Delta Lake stores the data in that cloud account location in Parquet format.
Can I copy my Delta Lake table to another location?
Yes you can copy your Delta Lake table to another location. Remember to copy files without changing the timestamps to ensure that the time travel with timestamps will be consistent.
Can I stream data directly into and from Delta tables?
Yes, you can use Structured Streaming to directly write data into Delta tables and read from Delta tables. See Stream data into Delta tables and Stream data from Delta tables.
