A Comprehensive Beginner's Guide to Databricks

The comprehensive guide to Databricks is an essential resource for anyone looking to leverage the power of Databricks for big data processing and analytics. The guide covers everything from getting started with Databricks to advanced topics like machine learning, deep learning, and natural language processing


Jatin Solanki

Updated on

June 15, 2024

Imagine creating an ad campaign without any demographic data about your target audience. Or creating a business plan without any market-related data. Seems impossible, right? For all business decisions, data-driven decision-making is becoming the norm. Every day, data warehouses and analytics tools receive a large amount of data, which is analyzed to gain insights. Companies require effective, reliable, scalable, and user-friendly workspaces for data scientists, data analysts, and data engineers. And here's where Databricks come into the picture. 

In layman's terms, Databricks is a cloud-based platform for data warehousing and machine learning. But that’s not all, it has much more to offer. It takes care of all your data needs. From data storage to analysis to driving insights, Databricks has got your back. In this blog, we are going to break down what Databricks is and what wonders it can do for your company.

What is Databricks? 

Databricks is a cloud-based platform developed by an enterprise software company that facilitates data engineering tools for processing and transforming massive amounts of data in order to create machine learning models.

Databricks is a single, cloud-based platform that takes care of all your data requirements. That means it's also a single platform where your whole team can work collaboratively. Databricks is quick, inexpensive, and automatically scales to huge data sets. It simplifies and merges your data systems for their seamless function. Contrary to traditional data processing, which is time-consuming and complicated to get started with, Databricks can be built on your existing cloud, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud. This undoubtedly makes it easier for businesses to manage an immense amount of data, visualize the same, and execute machine-learning algorithms (who knows, you can finally move away from just talking about it and actually creating some cool predictive models!). It improves innovation and development and also comes with better security options.   

Fundamental features of Databricks

Now that you know what Databricks is, let's dive into its features: 

  1. Supports multiple coding languages: Databricks provides a notebook interface for developers that supports multiple coding languages in the same computing environment. To build an algorithm using Python, R, Scala, or SQL, a developer just has to use some special commands like %python, %r, %scala, and %sql, and they are good to go. With this feature, developers can carry out different tasks with different coding languages, whichever works best for them. As an illustration, tasks involving data transformation can be carried out using Spark SQL, predictions can be made using Scala, model performance can be assessed using Python, and data can be visualized using R.
  2. Enhances productivity: It allows users to perform rapid installations of notebooks in production, thus boosting their productivity. Thanks to Databricks, data scientists, data analysts, and data engineers can all work together in a collaborative workspace. This collaborative environment encourages employees to come up with innovative ideas. It enables team members to make frequent adjustments while hastening the development process. With its built-in version control tool, Databricks tracks recent modifications and makes it easier to locate recent changes. Hence, saving time and efforts 
  3. Connecting with multiple data sources: Because Databricks connects to multiple data sources, the data analytics possibilities are endless. Databricks links to on-premises SQL servers, CSV servers, and JSON servers in addition to cloud storage services offered by AWS, Azure, or Google Cloud. MongoDB, Avro files, and many other types of files are also connected to the platform.

What is the purpose of Databricks? 

Companies receive tons of data each day. To process this data that comes in scheduled batches or real-time streams, many companies are using a complicated combination of data lakes and data warehouses, with parallel “pipelines”. Furthermore, it is layered on top of various tools for data science and analysis. It seems sluggish, doesn't it? With Databricks, you don't need any of that. Databricks is your one-stop solution for all your data requirements. 

Databricks can:

  • Keep all your data in one place
  • Seamlessly handle both batch and real-time data.
  • Organize data
  • Transform the data 
  • Analyze data collected
  • Utilize the data for the purpose of machine learning and AI
  • Generate reports to show insights 

Databricks can be used for the tasks mentioned above, or they can be combined with other technologies in your cloud data system.

Be it small businesses or large MNCs, Databricks is for all. It can be employed by the entire data team, including data scientists, data analysts, data engineers, business intelligence practitioners, and machine learning engineers. We think that’s a win for us busy bee data teams!

How to get started with Databricks?

Now let us understand how you can get started with Databricks to satisfy all your data requirements:

  • Step 1: Go to the Google Cloud Platform Marketplace and search for Databricks. Sign up for a free trial of Databricks. 
  • Step 2: Once you sign up for a trial subscription, you will get a link from the "Databricks" menu item in the Google Cloud Platform. You can manage setup on the Databricks-hosted account management page using this link.
  • Step 3: The next step is to create a workspace. It’s an environment in Databricks through which you can access your assets. For this, an external Databricks Web application is required. 
  • Step 4: To construct a workspace, you'll need three nodes in a Kubernetes cluster in your Google Cloud Platform project, and you'll use GKE to host the Databricks Runtime, which serves as your data plane. This distinction is necessary because your data always resides in your cloud account in the data plane and in your own data sources, not in the control plane, allowing you to keep control and ownership of your data. 
  • Step 5: After this step, create a table in Delta Lake. For this, you can upload a file, connect to supported data sources, or use a partner integration.
  • Step 6: Create a "cluster" to analyze your data. A Databricks Cluster is a blend of computation resources and configurations on which you can run jobs and notebooks. Streaming Analytics, ETL Pipelines, Machine Learning, and Ad-hoc analytics are a few tasks that you can run on the Databrick Cluster. 
  • Step 7: The cluster runtime in these Databricks is dependent on Apache Spark. The majority of Databricks' tools are built using open-source frameworks and libraries like MLflow and Delta Lake.

What are the benefits of using Databricks? 

  • Databricks gives an integrated Data Analytics Platform for data engineers, data scientists, data analysts, and business analysts.
  • It’s versatile across different ecosystems – AWS, GCP, and Azure.
  • Databricks ensures data reliability and scalability via delta lake.
  • Frameworks (sci-kit-learn, TensorFlow, Keras), libraries (matplotlib, pandas, NumPy), scripting languages (e.g.R, Python, Scala, or SQL), tools, and IDEs (JupyterLab, RStudio) are all supported by Databricks.
  • Databricks has basic inbuilt visualizations.
  • With the help of HYPEROPT, it’s possible to tune hyperparameters.
  • It is ten times faster than other ETLs.

Final thoughts 

Databricks has a lot of potential when it comes to data-related requirements. It can save time and effort for your data team and boost productivity. Companies require Databricks to analyze their business data stored in multiple data streams. To have a comprehensive view of the data, the data must be imported into the data warehouse.

Now that you are fully aware of what Databricks is, its features and benefits, and know how to get started, I hope nothing is holding you back. Get started today!

Once you have databricks deployed, its time to observe the data where decube is the right partner for that. Signup now!

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