Databricks is a modern big data processing technology. It is a one-stop solution for all organizational data needs, such as data warehousing, AI, machine learning, data visualization, operational use cases, etc. In this Databricks tutorial, we will understand the basic building blocks of the modern data technology stack, Databricks' role, architecture, use cases, advantages, and many more. Let's jump into the details.
The amount of data generation has been growing at an alarming rate. Businesses must use this big data to stay competitive, find opportunities, make decisions, and more. Typical businesses serve their customers with different products and services, and all the data, such as supplier information, sales data, marketing data, distribution data, etc., need to be processed in real-time to support various departments.
The data engineering teams collect, cleanse, and store data in data warehouse platforms. Using a traditional technology stack, processing information from a source takes days to weeks without using Spark. Unlike other systems, Databricks processes large volumes of data in real-time and supports typical business operations on the fly.
Databricks is a leading big data processing platform that processes enormous volumes of data in real-time. It acts as a powerful centralized data processing platform. It brings all data processing technologies under one roof, which includes ETL, BI, AI, ML, etc. Using Databricks, organizations can source, store, clean, and visualize massive volumes of data.
It allows easy integration between various data disciplines, such as data engineering, analytics, and data science, making data processing much more accessible, from preparation to experimentation.
Databrisks can be deployed on top of major cloud providers such as AWS, Azure, and GCP. Its seamless integration process allows easy integration with diversified data sources, third-party solutions, developer tools, etc.
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The common challenge today's organizations face is bringing together and finding value in big data from diversified sources. That's where Databricks steps in, it allows organizations to extract data from a wide range of sources, clean it, and made it available to BI, AI, and machine learning models.
Databricks streamlines the process of building a modern cloud data warehouse for self-service analytics and offers high performance and governance.
Databricks is a combination of four open-source tools combined into one single platform and delivered as a service from the cloud. Let's understand more about these four open-source platforms.
Related Article: Databricks Workspace
Among the four open-source tools, Apache Spark is the core component of Databricks, a big data processing engine. Spark is a mighty big data processing platform specialized in large-scale distributed processing on massive datasets. It utilizes optimized querying and in-memory caching for faster query execution on any dataset. The reason behind faster data processing is that it runs on memory (RAM), whereas other systems are on disk drives.
Apache Spark can be used for core things such as building data pipelines, running distributed SQL, running machine learning algorithms, ingesting data into a database, batch and real-time data processing, etc.
Delta Lake is another open-source component of Databricks that stores data on Lake House platforms. It is an optimized default storage layer to store data and tables in the Databricks lakehouse.
Delta Lake can easily integrate with Apache Spark APIs and handle structured streaming. Structured steaming supports both batch and streaming operations at scale. Moreover, it enhances the performance and manageability of data stored in cloud storage objects.
MLFlow is another open-source framework of Databricks and manages the end-to-end lifecycle of machine learning applications and pipelines.
Data scientists are the people who look after ML operations in any enterprise, and it is one of the most complex jobs in data science. They perform various tasks such as deploying ML models, running experiments, training algorithms, tracking, and code packaging. Managing these operations, feeding suitable data sources to ML algorithms, and getting reliable insights is challenging.
Databricks MLFlow simplifies the process for data scientists and offers core components such as Tracking, Models, Projects, Model Registry, Model Serving, security, high availability, model training, and many other options to make your ML project successful.
Pandas is a widely used package by data scientists and offers various data structures and analysis tools. When it comes to big data processing, there are some issues with pandas, and they are not easily scalable. Kolas fills this gap by delivering Scalable data structures compatible with Apache Spark.
Kolas not only replaces and is helpful for pandas users but also helps Pyspark users perform complex operations. Moreover, It minimizes the learning curve for data scientists and improves productivity.
So far, we have discussed the core aspects of the Databricks platform; now it's time to learn about some of its core features and their role in simplifying big-data processing; the following are some of the core Databricks features.
Databricks offers a Notebook interface, which allows code to be written in multiple languages. Notebook allows developers to build algorithms using any programming language such as Python, Scala, SQL, or R. For instance, model performance can be done using Python, model prediction by Scala, Spark SQL transformations, visualization using R, etc.
Databricks offers a collaborative environment for data engineers, scientists, business analysts, etc, to work on things and bring insights. Moreover, it has built-in versioning features, allowing users to track changes easily.
Databricks is built on top of Apache Spark and is flexible enough to align with any cloud platform. It can handle small jobs such as development, testing, etc., and also executes big data jobs. It shuts down clutter if not in use and scales automatically when required.
Databricks can easily connect to cloud providers such as AWS, Google Cloud, and Azure and also works with on-prem services such as SQL server, JSON, CSV, etc. It also connects to various file types and allows the developers to perform analytical tasks on them.
Conclusion
With this, you may have a fair understanding of Databricks, its core components and features, and how it works. Very soon, we will update this Databricks tutorial with many other concepts like Databricks architecture, Databricks use cases, applications, and a lot more. Moreover, Databricks has become one of the best and most efficient choices for companies because of its features and high flexibility in coordinating with multiple data engineering departments.
By Tech Solidity
Last updated on February 12, 2024
