What Is Databricks? – Components, Features, Architecture

In this article, We are going to discuss What Is Databricks? and what are their Components, Features, and Architecture of it.

Understanding spark is crucial in data engineering tasks, I hope you have got a decent understanding of Spark, now let’s understand what is Databricks.

Databricks is simple to use fast data execution and collaborative Apache Spark-based Centralized data processing and analytics platform built on the cloud system.

The data is distributed and parallel processed in memory of multiple nodes in an exceeding cluster because it’s supported Spark execution Engine.

It has support for all the Spark use cases machine learning, instruction execution, stream processing, advanced analytics, etc., and similar like spark data bricks also supports All the languages like Scala, Python, SQL, R, or Java.

Different Components of Data bricks

1. Cluster

It is the most significant component in databanks and spark for data execution at a much faster space In a Spark cluster, has two types of nodes, worker nodes, the nodes that perform the task of data processing.

Since data in Spark is processed in parallel, having more worker nodes may help in faster processing.

And driver node is responsible for making the request, distributing the task to worker nodes, and coordinating the execution.

There are two types of clusters you can create in Databricks, an interactive cluster that allows multiple users to interactively explore and analyze the data, and a job cluster that is used to run fast and automated jobs.

2. Workspace

A workspace is a place where you can manage all the data or files in a folder format, which can be notebooks, different libraries, Visualization dashboards, ML experiments, etc.

You can define fine-grained access control on all these objects, allowing users to use the same workspace, but only giving them restricted access.

There are multiple version control options available in the data bricks workspace like GitHub, Bitbucket, Azure DevOps, etc.

3. Notebook

Notebook is the tool where you can write and execute the code or perform different data transformations on data with Spark-supported languages.

In the notebook of data bricks, you can write code in python, SQL, Scala, R, or Java, in a single notebook means you can write code in these various languages in a single notebook together.

So you can write the transformation logic in python or SQL and extract data using Scala, python in the same notebook.

On the other hand, you can build the workflow of data with end to end system by invoking the notebooks with another one this can help in creating end-to-end workflows.

With the help of an interactive cluster, you can run queries or you can run the complete notebooks using jobs and notebooks that also support built-in visualization.

4. Jobs.

Jobs allow the execution of a notebook, or if you have an external JAR file that you would like to execute on a Spark cluster, you can do that using jobs.

A job can run immediately, or it can be scheduled. And by now you know that jobs can run on job clusters.

Job clusters are created and terminated with the job, but if you have a running interactive cluster, you can run these jobs on them as well.

Each job can also have a different cluster configuration on which it can run, This allows using a smaller cluster for the smaller jobs and a large cluster for the bigger ones.

And finally, you can completely monitor this kind of job run, retry on failures, and set up alerts for notification.

5. Libraries

sometimes you need to use third-party libraries in your data projects You can install these libraries on the Spark cluster, and they can be in any Spark-supported language.

After the installation of libraries on the cluster, you can refer to these libraries in your notebooks and  A library can be scoped at the cluster level.

which means it only exists in the context of a cluster, or you can install and scope the library at the notebook level.

6. Databases and tables

If you are coming from a relational database background, you’ll be really excited to see, that you can create databases and tables inside these databases.

Databases and tables are distinct from relational databases and a table in Databricks represents a collection of structured data.

This means the table has a structure, it has columns, and columns have a datatype. This table is equivalent to a DataFrame because a DataFrame also has a structure.

This means any operation that you can perform on DataFrame, you can do the same on a table. A table is created using the file present on the storage.

So, in effect, it’s just a representation of an underlying file where you know the schema. Any change in the file will also affect the table.

The Important Features of Databricks

1. Collaboration

While working with Databricks, you furthermore may get a workspace with different users within the data analytics teams like data engineers, data scientists, and business analysts.

All the mentioned people can work together here and they can share the code and datasets, explore and visualize the info, post comments, and integrate with source control.

2. Infrastructure Management

But together with all the Spark functionality, Databricks brings several features to the table. First, and I believe the foremost important one, is infrastructure management.

Since Spark is an engine, so to figure with it, you would like to line up a cluster, install Spark, handle the scalability, physical hardware failures, upgrades, and far more.

But with Databricks, you’ll launch an optimized Spark environment with just some clicks and auto-scale it on demand

3. Build in Security

Security is the essential part of any data-related tool and Databricks comes with built-in access control and enterprise-grade security.

It complies with high system security so that you can securely able to deploy your applications to production on data bricks.

4. Automation of Task

Task automation and execution is the crucial factor in any data-related task because it avoids human error and deployment time.

After completion of the data exploring and data pipeline building process, It can automate the execution plan.

Also, you can simply execute them based on your requirement you can automate the execution on a scheduled process.

Databricks Architecture

Let’s have a look at the architecture of Databricks. It is divided into three important layers, the Cloud Service, the Runtime, and the Workspace. Let’s understand each layer with components.

1. Cloud Service.

Databricks are highly used for three main cloud-based platforms AWS, Microsoft Azure, and Google cloud, etc, using virtual machine environments to execute tasks.

The virtual machine is used to configure multiple clusters at the time for multiple task execution or we can say that distributed processing.

Databricks File system

It is one of the prominent features of Databricks with native support of a distributed file system and it is required to persist the data.

Whenever you create a cluster in Databricks, it mostly comes with preinstalled Databricks File System called DBFS.

The important point to note is that DBFS is just an abstraction layer, and it uses Azure Blob storage at the back end to persist the data.

Azure Storage or blob storage is an azure cloud account that you see here is mounted to DBFS by default, you can connect multiple Azure Data Lake Store or Azure Storage.

2. Databricks Runtime

This is the second and most important layer the Databricks called data bricks Runtime which is the collection of different data bricks services like Apache spark, Delta Lake, data bricks I/O, serverless cluster, etc.

Apache Spark

Each Runtime version of apache spark comes with a specific bundled, with some additional set of advancements and optimizations.

In Azure, Databricks runs on the Ubuntu Operation system which means runtime comes with system libraries of Ubuntu, and All the languages with their corresponding libraries are preinstalled.

For example, If you are interested to work on machine learning then all the libraries are preinstalled for ML, similarly, if you need  GPU-enabled clusters then GPU libraries are installed.

Good thing is that versions of these libraries that are installed with Runtime work well with each other, preventing the trouble of manual configuration and compatibility issues.

This means you can run the same code on different versions of Spark, making it easier to upgrade or test the performance.

Databricks I/O

It Is also called DBIO in data bricks which is a module that brings different levels of optimizations on top of Spark related to caching, file decoding, disk read/write, etc. and you can control these optimizations also.

The significant point is that workloads running on Databricks can perform 10 times faster than vanilla Spark deployments.

Now even though you can create multiple clusters in Databricks, doing so adds to cost, so you would want to maximize the usage of the clusters.

Databricks Serverless clusters

It is also called high concurrency clusters and has an automatically managed shared pool of resources that enables multiple users and workloads to use it simultaneously.

But you might think, what if a large workload like ETL consumes a lot of resources and blocks other users’ short and interactive queries? Your question is very valid.

That’s why each user in a serverless cluster gets a fair share of resources, complete isolation, and security from other processes without doing any manual configuration or tuning.

This improves cluster utilization and provides another 10x performance improvement over native Spark deployments.

To use Databricks Serverless, you will have to create a high concurrency cluster instead of a standard one, which you will see in the next module.

Databricks Runtime ML

Databricks also provide native support for various machine learning frameworks via Databricks Runtime ML.

It is built on top of Databricks Runtime, so whenever you want to enable machine learning, you need to select Databricks Runtime ML while creating the cluster.

The cluster then comes preinstalled with libraries like TensorFlow, PyTorch, Keras, GraphFrames, and more.

And it also supports third-party libraries that you can install on the cluster, like scikit-learn, XGBoost, DataRobot, etc.

Delta Lake

In data bricks, the very interesting component is Delta Lake which is built by the Databricks team and it was Databricks Delta, but now this component is open-source called Delta Lake.

Now most of the teams still using Data Lakes, but they struggle to manage them as the files in Data Lake and they don’t have the great features of relational tables.

Delta Lake is an open-source storage layer that gives features to Data Lake, which is very close to relational databases and tables, and much beyond that, like ACID transaction support where multiple users can work with the same files and get ACID guarantees.

You can do different DML operations like insert, update, delete, and merge, and also some time travel operations like keeping snapshots of data enabling audits and rollbacks, etc.

3. Databricks Workspace.

In the data bricks workspace, two-part have been created which handle the workspace and production of spark execution jobs.

The first one is an interactive workspace and the second one is the data bricks production let’s check each one separately in the details.

Interactive workspace

In this environment, you can explore and analyze the data interactively just like you open an Excel file, apply the formula, and see the results immediately.

In the same way, you can do complex calculations and interactively see the results in the workspace. You can also render and visualize the data in the form of charts.

In Databricks Workspace, you get a collaborative environment. Multiple people can write code in the same notebook, track the changes to the code, and push them to source control when done.

 And datasets that you have processed can be put together on a dashboard. It could be for the end-users, or these dashboards can also be used to monitor the system. You will learn about the components that enable these features in just a minute.

Databricks Production

After done with data exploration, you can now build end-to-end workflows by orchestrating the notebooks.

These workflows can then be deployed as Spark jobs and can be scheduled using the job scheduler then you can monitor these jobs, check the logs, and set up alerts.

Similarly, in the same workspace, you cannot just interactively explore the data but you can also take it to production with very little effort.

Conclusion

Databricks provide a service to perform the execution of data in an optimized version of Spark securely on a cloud platform.

With the help of data bricks, you can create multiple clusters, and the cluster resources can be efficiently shared with multiple users and workloads.

It connects or integrates the workspace and activities of data engineers, data scientists, analysts, etc. to increase the productivity of work.

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