Introduction
Data is becoming increasingly more important, and businesses that are able to harness the power of data are the ones that are going to be successful in the future.
So how do you go about creating a data-driven business? The pace at which the data and the variety of sources is increasing is incomprehensible. Costs are mounting up for Traditional Data Warehouses which are also not capable of handling unstructured data and other modern use cases of analytics including AI/ML use cases.
So where is the next wave going, we feel the first step is to create a data lake. A data lake is a place where you can store all of your data in one central location. This can include data from internal and external sources, such as customer data, social media data, and website data.
Fission Labs has worked extensively on Data Lakes using Databricks as it is a platform that allows you to easily create and manage data lakes closing into PetaBytes of data. With its powerful data lake house architecture, you can easily store, process, and analyze huge amounts of data. This can help you to make better decisions, faster.
What is Databricks?
Databricks is a powerful platform for data processing and analysis.
With Databricks, you can easily build a “Data Lakehouse” architecture, which is a term for a data management architecture combining the scale and flexibility of data lakes along with ACID transactions of Data warehouse (and hence the term “lakehouse”).
Databricks also offers a variety of features that make data processing and analysis easier, including:
- Job Workflows is a graphical interface that makes it easy to create and schedule flows for data processing and analytics.
- A variety of libraries for data analysis, includingSpark, Java, Python, R, and Scala.
- The ability to run notebooks in parallel to speed up data processing
Providing a single interface for all these languages and adding a new Notebook interface contributes to optimized and shorter development lifecycles. You can use Databricks to run SQL queries, create machine learning models, and perform data engineering tasks with ease.
Databricks is built by the creators of Apache Spark, and because the platform is built on top of Apache Spark, you can be sure that it will always be up-to-date with the latest features and advancements.
Medallion Architecture
Databricks popularized the medallion architecture, which is a design pattern for incrementally processing and improving the data. The data flows from the Bronze, Silver to Gold layer, which are referred to as the stages of processing.
The raw data is first ingested into the Bronze Layer, which is also the landing zone for the data, this is your copy of the source. The sources can be streaming wherein you can ingest the data from Kafka topics or any other streaming solution like AWS Kinesis streams, or they can reside in S3.
Next you apply various data cleansing techniques, which may include some rudimentary data cleaning to more sophisticated inferences from machine learning algorithms. Some examples of data processing algorithm scan be data normalization for example for location, wherein you apply geocoding to string based location inputs. Another example can be to extract content from raw text using Named Entity Recognition (NER) techniques etc.
Once you have a clean source of data, the transformed data sits in the silver layer, and this is what we call the “Single Source of Truth”, or Silver Layer in theMedallion Architecture parlance. The data is now ready for analytics and other downstream applications.
Sometimes it is useful to have various perspectives of your “Single Source of Truth”, these perspectives are business-level aggregates which are processed and stored in the Gold Layer.
Depending on the nature of data, and its source, it would also be feasible to make this pipeline active using Delta Live Tables, which can instantly and incrementally mutate the data in all these layers.
How to Implement a Data Lakehouse on Databricks
Databricks is a cloud-based platform that allows you to build, monitor, and manage data pipelines. It also provides a number of built-in algorithms and libraries that make it easy to process and analyze data.
To create a data lakehouse on Databricks, you will first need to create a Databricks cluster. A cluster is a group of nodes, or virtual machines, that are used to run your data pipelines. Once you have created a cluster, you can then add data to it.
Databricks offers a number of different storage options for your data, including S3, Azure Blob Storage, and HDFS. You can also use Databricks to process streaming data in real-time.
Using Unity Catalog you can adopt a unified governance solution for all your data assets stored in S3. We advise creating Delta Tables on Unity Catalog for better management of data and also leveraging the full power of the Databricks Lakehouse architecture.
Once your data is stored in Databricks and represented as Delta Tables, you can then use the platform's powerful query engine to run analytics on it. Databricks also makes it easy to visualize your data, so you can see patterns and trends that you might not have noticed otherwise.
And thanks to Databricks’ intuitive platform, you can easily build infrastructure as code and quickly deploy services in the cloud, allowing you to scale up or down as needed. Plus, with the ability to use best-in-class machine learning algorithms on Spark clusters and automatically provision compute for those workloads, you’ll be able to optimize for cost and performance.
Databricks and Machine Learning
The Databricks Data Lakehouse architecture bridges the gap between data engineering and data science. By combining the best of both worlds, you can easily build a data-driven business from the ground up. From storing and managing the raw data to curating the data for machine learning models, this architecture streamlines the entire process.
With Databricks, you can easily build and train machine learning models on your data and implement the best practices like MLFlow, without having to worry about the underlying infrastructure.
Plus, Databricks integrates with popular machine learning libraries like TensorFlow and PyTorch, so you can use the models you already have. And if you need help getting started, Databricks offers comprehensive documentation and support.
How Databricks can Benefit your Business
The Databricks platform offers a range of features and tools that make it easy to get started. You can quickly build data pipelines and models, and then deploy them in minutes. Plus, you can use the built-in intelligence to automatically optimize your workloads and accelerate your time-to-value.
Databricks’ Lakehouse architecture offers the perfect solution for businesses seeking to be more data-driven. With a data lakehouse, you can collect, store, and process all types of disparate data from different sources in one place. Furthermore, the lakehouse model makes it easier to work with multiple data sets, allowing you to quickly and easily access the insights you need.
The Lakehouse architecture also enables you to use various analytics techniques for a more comprehensive view of your data. The end result is greater insight into your customers’ needs and behaviors which can then be used to inform business decisions and strategies. In this way, adopting a Lakehouse architecture provides a major advantage when it comes to understanding your customers’ needs and driving more results from your business.
With Databricks, you can trust your data to drive your business forward.
Conclusion
In order to create a data-driven business, you need to have data that is accessible, usable, and valuable. Databricks is a cloud-based data platform that helps you accomplish all three. With Databricks, you can build data pipelines, run machine learning algorithms, and create visualizations to gain insights from your data. Databricks is the only platform that combines data engineering, data analytics, and business intelligence in one place. This makes it the perfect platform for businesses of all sizes to get started with data-driven decision making.
If you're interested in implementing Databricks solutions in your own organization, Contact Us today.We can help you get started with this exciting new technology and unleash the power of data-driven innovation.
Content Credit: Mohit Singh, Satya Srikkant Mantha