Data-driven decision-making has become the key to all business decisions in companies. Companies use Data Analysis and Big Data to make these decisions. Large volumes of data flow from different source systems to the Data Warehouse or any Analytics tool to get insights from it. Companies are in need of a fast, reliable, scalable, and easy-to-use workspace for Data Engineers, Data Analysts, and Data Scientists. This is where you will need to understand what is Databricks?
Databricks is basically a Cloud-based Data Engineering tool that is widely used by companies to process and transform large quantities of data and explore the data. This is used to process and transform extensive amounts of data and explore it through Machine Learning models. It allows organizations to quickly achieve the full potential of combining their data, ETL processes, and Machine Learning.
From this blog, you will get to know the Databricks Overview and What is Databricks. The key features and architecture of Databricks are discussed in detail. In this blog on What is Databricks, the steps to set up Databricks are briefly explained. The benefits and reasons for the Databricks platform’s need are also elaborated in this blog on what is Databricks.
Table of Contents
Databricks is an enterprise software company that provides Data Engineering tools for Processing and Transforming huge volumes of data to build machine learning models. Traditional Big Data processes are not only sluggish to accomplish tasks but also consume more time to set up clusters using Hadoop. However, Databricks is built on top of distributed Cloud computing environments like Azure, AWS, or Google Cloud that facilitate running applications on CPUs or GPUs based on analysis requirements. Databricks platform is said to be 100 times faster than Apache Spark. It enhances innovation and development and also provides better security options. It comes with inbuilt data visualization tools. Let us dive deep into knowing What is Databricks.
What is Databricks?
Let us start by answering this main question of What is Databricks. Databricks, developed by the creators of Apache Spark, is a Web-based platform, which is also a one-stop product for all Data requirements, like Storage and Analysis. It can derive insights using SparkSQL, provide active connections to visualization tools such as Power BI, Qlikview, and Tableau, and build Predictive Models using SparkML. Databricks also can create interactive displays, text, and code tangibly. Databricks is an alternative to the MapReduce system.
Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform, making it easy for businesses to manage a colossal amount of data and carry out Machine Learning tasks.
It deciphers the complexities of processing data for data scientists and engineers, which allows them to develop ML applications using R, Scala, Python, or SQL interfaces in Apache Spark. Organizations collect large amounts of data either in data warehouses or data lakes. According to requirements, data is often moved between them at a high frequency which is complicated, expensive, and non-collaborative.
However, Databricks simplifies Big Data Analytics by incorporating a LakeHouse architecture that provides data warehousing capabilities to a data lake. As a result, it eliminates unwanted data silos created while pushing data into data lakes or multiple data warehouses. It also provides data teams with a single source of the data by leveraging LakeHouse architecture.
What is Databricks?: Database Workspace
An Interactive Analytics platform that enables Data Engineers, Data Scientists, and Businesses to collaborate and work closely on notebooks, experiments, models, data, libraries, and jobs.
What is Databricks?: Databricks Machine Learning
An integrated end-to-end Machine Learning environment that incorporates managed services for experiment tracking, feature development and management, model training, and model serving. With Databricks ML, you can train Models manually or with AutoML, track training parameters and Models using experiments with MLflow tracking, and create feature tables and access them for Model training and inference.
You can now use Databricks Workspace to gain access to a variety of assets such as Models, Clusters, Jobs, Notebooks, and more.
What is Databricks?: Databricks SQL Analytics
A simple interface with which users can create a Multi-Cloud Lakehouse structure and perform SQL and BI workloads on a Data Lake. In terms of pricing and performance, this Lakehouse Architecture is 9x better compared to the traditional Cloud Data Warehouses. It provides a SQL-native workspace for users to run performance-optimized SQL queries. Databricks SQL Analytics also enables users to create Dashboards, Advanced Visualizations, and Alerts. Users can connect it to BI tools such as Tableau and Power BI to allow maximum performance and greater collaboration.
What is Databricks?: Databricks Integrations
As a part of the question What is Databricks, let us also understand the Databricks integration. Databricks integrates with a wide range of developer tools, data sources, and partner solutions.
- Data Sources: Databricks can read and write data from/to various data formats such as Delta Lake, CSV, JSON, XML, Parquet, and others, along with data storage providers such as Google BigQuery, Amazon S3, Snowflake, and others.
- Developer Tools: Databricks supports various tools such as IntelliJ, DataGrip, PyCharm, Visual Studio Code, and others.
- Partner Solutions: Databricks has validated integrations with third-party solutions such as Power BI, Tableau, and others to enable scenarios such as Data Preparation and Transformation, Data Ingestion, Business Intelligence (BI), and Machine Learning.
Hevo Data is a No-code Data Pipeline that offers a fully-managed solution to set up data integration from 100+ Data Sources (including 40+ Free Data Sources) and will let you directly load data to Databricks or a Data Warehouse/Destination of your choice. It will automate your data flow in minutes without writing any line of code. Its Fault-Tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.
Its completely automated Data Pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
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- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Connectors: Hevo supports 100+ Integrations to SaaS platforms, Files, Databases, BI tools, and Native REST API & Webhooks Connectors. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt, Data Warehouses; Amazon S3 Data Lakes; Databricks; and MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (Including 40+ Free Sources) that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
Key Features of Databricks
After getting to know What is Databricks, let us also get started with some of its key features. Below are a few benefits of Databricks:
- Language: It provides a notebook interface that supports multiple coding languages in the same environment. Using magical commands (%python, %r, %scala, and %sql), a developer can build algorithms using Python, R, Scala, or SQL. For instance, data transformation tasks can be performed using Spark SQL, model predictions made by Scala, model performance can be evaluated using Python, and data visualized using R.
- Productivity: It increases productivity by allowing users to deploy notebooks into production instantly. Databricks provides a collaborative environment with a common workspace for data scientists, engineers, and business analysts. Collaboration not only brings innovative ideas but also allows others to introduce frequent changes while expediting development processes simultaneously. Databricks manages the recent changes with a built-in version control tool that reduces the effort of finding recent changes.
- Flexibility: It is built on top of Apache Spark that is specifically optimized for Cloud environments. Databricks provides scalable Spark jobs in the data science domain. It is flexible for small-scale jobs like development or testing as well as running large-scale jobs like Big Data processing. If a cluster is idle for a specified amount of time (not-in-use), it shuts down the cluster to remain highly available.
- Data Source: It connects with many data sources to perform limitless Big Data Analytics. Databricks not only connects with Cloud storage services provided by AWS, Azure, or Google Cloud but also connects to on-premise SQL servers, CSV, and JSON. The platform also extends connectivity to MongoDB, Avro files, and many other files.
Databricks is the application of the Data Lakehouse concept in a unified cloud-based platform. Databricks is positioned above the existing data lake and can be connected with cloud-based storage platforms like Google Cloud Storage and AWS S3. Understanding the architecture of databricks will provide a better picture of What is Databricks.
Layers of Databricks Architecture
- Delta Lake: Delta Lake is a Storage Layer that helps Data Lakes be more reliable. Delta Lake integrates streaming and batch data processing while providing ACID (Atomicity, Consistency, Isolation, and Durability) transactions and scalable metadata handling. Furthermore, it is fully compatible with Apache Spark APIs and runs on top of your existing data lake.
- Delta Engine: The Delta Engine is a query engine that is optimized for efficiently processing data stored in the Delta Lake.
- It also has other inbuilt tools that support Data Science, BI Reporting, and MLOps.
All these components are integrated as one and can be accessed from a single ‘Workspace’ user interface (UI). This UI can also be hosted on the cloud of your choice.
Why Databricks Platform is a Big Deal?
After getting to know What is Databricks, you must know why it is claimed to be something big. Databricks platform is basically a combination of four open-source tools that provides the necessary service on the cloud. All these are wrapped together for accessing via a single SaaS interface. This results in a wholesome platform with a wide range of data capabilities.
- Cloud-native: Works fine on any prominent cloud provider
- Data storage: Stores a broad range of data including structured, unstructured, and streaming
- Governance and management: In-built security controls and governance
- Data science tools: Production-ready data tooling from engineering to BI, AI, and ML
All these layers make a unified technology platform for a data scientist to work in his best environment. Databricks is a cloud-native service wrapper around all these core tools. It pacifies one of the biggest challenges called fragmentation. The enterprise-level data includes a lot of moving parts like environments, tools, pipelines, databases, APIs, lakes, warehouses. It is not enough to keep one part alone running smoothly but to create a coherent web of all integrated data capabilities. This makes the environment of data loading in one end and providing business insights in the other end successful.
Databricks provides a SaaS layer in the cloud which helps the data scientists to autonomously provision the tools and environments that they require to provide valuable insights. Using Databricks, a Data scientist can provision clusters as needed, launch compute on-demand, easily define environments, and integrate insights into product development.
Databricks Getting Started
In this blog on What is Databricks, Get to know the steps to set up Databricks to start using it. Generally, Databricks offer a 14-day free trial that you can run on your preferable cloud platforms like Google Cloud, AWS, Azure. In this tutorial, you will learn the steps to set up Databricks in the Google Cloud Platform.
Step 1: Search for ‘Databricks’ in the Google Cloud Platform Marketplace and sign up for the free trial.
Step 2: After starting the trial subscription, you will receive a link from the Databricks menu item in Google Cloud Platform. This is to manage setup on the Databricks hosted account management page.
Step 3: After this step, you must create a Workspace which is the environment in Databricks to access your assets. For this, you need an external Databricks Web Application (Control plane).
Step 4: To create a workspace, you need three nodes Kubernetes clusters in your Google Cloud Platform project using GKE to host the Databricks Runtime, which is your Data plane.
It is required to ensure this distinction as your data always resides in your cloud account in the data plane and in your own data sources, not the control plane — so you maintain control and ownership of your data.
Step 5: Next to create a table in the Delta Lake, you can either upload a file, or connect to supported data sources, or use a partner integration.
Step 6: Then to analyze your data you must create a ‘Cluster‘. A Databricks Cluster is a combination of computation resources and configurations on which you can run jobs and notebooks. Some of the workloads that you can run on a Databricks Cluster include Streaming Analytics, ETL Pipelines, Machine Learning, and Ad-hoc analytics.
Step 7: In these Databricks, the runtime of the cluster is based on Apache Spark. Most of the tools in Databricks are based on open source technologies and libraries such as Delta Lake and MLflow.
Benefits of Databricks
After getting to know What is Databricks, let’s discuss more on its benefits.
- Databricks provides a Unified Data Analytics Platform for data engineers, data scientists, data analysts, business analysts.
- It has great flexibility across different ecosystems – AWS, GCP, Azure.
- Data reliability and scalability through delta lake are ensured in Databricks.
- Databricks supports frameworks (sci-kit-learn, TensorFlow, Keras), libraries (matplotlib, pandas, NumPy), scripting languages (e.g.R, Python, Scala, or SQL), tools, and IDEs (JupyterLab, RStudio).
- Using MLFLOW, you can use AutoML and model lifecycle management.
- It has got basic inbuilt visualizations.
- Hyperparameter tuning is possible with the support of HYPEROPT.
- It has got Github and bitbucket integration
- Finally, it is 10X Faster than other ETL’s.
Role-based Databricks adoption
In this context of understanding what is databricks, it is also really important to identify the role-based databricks adoption.
Data Analyst/Business analyst
For a Business analyst, visualization plays a pivotal role. So it requires a BI integration and also Databricks SQL is required.
Data Scientists are mainly responsible for sourcing data, a skill grossly neglected in the face of modern ML algorithms. They must also build predictive models, manage model deployment, and model lifecycle.
Data Engineers are mainly responsible for building ETL’s and managing the constant flow of data. They have to process, clean, and quality checks the data before pushing it to operational tables. Model deployment and platform support are other responsibilities entrusted to data engineers.
Databricks have to be combined either with Azure/AWS/GCP and due to its relatively higher costs, adoption of it in small/medium startups is quite low in India.
From this blog on what is databricks, you will get to know the Databricks Overview and its key features. The blog answers your main question on What is Databricks. The architecture of Databricks is discussed in detail. From this blog on What is Databricks, the steps to set up Databricks will be all clear for you to get started. The benefits and reasons for the Databricks platform’s need are also elaborated in this blog on what is Databricks.
After understanding completely What is Databricks, what are you waiting for! Get started! Companies need to analyze their business data stored in multiple data sources. The data needs to be loaded to the Data Warehouse to get a holistic view of the data.
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Hevo Data is a No-code Data Pipeline that assists you in seamlessly transferring data from a vast collection of sources into a Data Lake like Databricks, Data Warehouse, or a Destination of your choice to be visualized in a BI Tool. It is a secure, reliable, and fully automated service that doesn’t require you to write any code!
If you are using Databricks as a Data Lakehouse and Analytics platform in your business after understanding What is Databricks and searching for a stress-free alternative to Manual Data Integration, then Hevo can effectively automate this for you. Hevo with its strong integration with 100+ Data Sources & BI tools (Including 40+ Free Sources), allows you to not only export & load Data but also transform & enrich your Data & make it analysis-ready.
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