Databricks: the ultimate guide for beginners

Databricks offers itself today as a key solution for companies. Indeed, companies have started to collect large amounts of data from many different sources. Therefore, they need more and more a unique system to store them.

Table of Contents

What is Databricks?

DataBricks is a cloud-based data engineering tool. Companies use it to process, transform and explore large amounts of data through machine learning models, among other things.

DataBricks was developed by the creators of Apache Spark. It is primarily a web platform. However, it is also a unique product that brings together storage and analysis. Databricks integrates with distributed cloud environments including Microsoft Azure, Amazon Web Services and Google Cloud Platform. On the one hand, running applications on CPUs or GPUs is faster. On the other hand, companies can more easily manage large amounts of data and perform machine learning tasks. Databricks improves innovation and development, while offering better security.

What are its features?

DataBricks features include language, productivity, flexibility, data source and integrations.

The language

DataBricks provides a notepad interface that supports multiple coding languages in the same environment. Users are provided with Python, R, Scala or SQL.

Productivity

This is a interactive analysis platform. This one provides a collaborative environment with a common workspace for data scientists, engineers and business analysts. They have the opportunity to collaborate on notebooks, experiments, models, data, libraries and tasks. The collaboration not only brings innovative ideas, but also allows others to introduce frequent changes. At the same time, development processes are accelerated.

Furthermore, Databricks manages recent changes with an integrated version control tool that reduces the effort of searching for recent changes.

Flexibility

Apache Spark built DataBricks. It therefore provides scalable Spark tasks In the data science domain. It is flexible for small-scale jobs such as development or testing. Nevertheless, it is also flexible for running large-scale jobs such as Big Data processing. If a cluster is idle for a specified time (not in use), it shuts down the cluster to remain highly available.

The data source

DataBricks connects to many data sources to perform unlimited big data analysis. It can read and write data from and to a variety of formats. In addition to AWS, Azure and Google Cloud, it also connects to on-premises SQL, CSV and JSON, XML, Parquet, Delta Lake servers. The platform extends connectivity to MongoDB, Avro files and other encores.

Integrations

For development tools, Databricks supports various equipment. These are IntelliJ, DataGrip, PyCharm, Visual Studio Code and others.

Note that Databricks has also validated integrations with third-party solutions such as Power BI, Tableau. This encourages a few scenarios namely data preparation and transformation, data ingestion, business intelligence (BI) or machine learning.

The basic principles of the Databricks platform

Organizations collect large amounts of data in data warehouses or data lakes. Depending on the 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 an architecture LakeHouse. The latter provides data warehousing capabilities to a data lake. It, therefore, eliminates unwanted data silos created when sending data. At the same time, it provides a single source of data by leveraging the LakeHouse architecture.

Data Warehouse

Data warehouses were designed to bring together the organization’s various data sources.

Data lakes

Data lakes are used to store large amounts of structured, semi-structured and unstructured data in their raw formats.

Data Lakehouse

The DataBricks Data Lakehouse is very advantageous. Firstly, it has metadata layers for data lakes. It is a means of tracking table versions, data descriptions and the application of their validation standards.

Second, it offers new conceptions of query engines. It enables high performance SQL execution on data lakes, for example Apache Spark. And thirdly, DataBricks makes optimal access to tools Data science and machine learning tools. Yet, this makes the processed data available in open data formats suitable for ML.

Databricks sits on top of your existing data lake, it can also connect to a variety of popular cloud storage offerings such as AWS S3 and Google Cloud Storage.

The platform’s architecture layers

Understanding the DataBricks architecture makes it clearer what it is.

Delta Lake

Delta Lake is a storage layer that maximizes the reliability of data lakes. It runs on top of the existing data lake and is fully compatible with the Apache Spark APIs. Delta Lake integrates continuous and batch data processing. In addition, this DataBricks layer provides transaction ACID (atomicity, consistency, isolation and durability) and scalable metadata management.

Delta Engine

The Delta Engine is a query engine optimized to efficiently process data stored in Delta Lake. It also has other built-in tools that support data science, BI reporting and MLOps.

All of these components are integrated into one and are accessible from a single “Workspace” user interface (UI).

Why is DataBricks important?

DataBricks brings together 4 open source tools that provides the necessary service on the cloud.

First and foremost is the native cloud. It works very well on any leading cloud provider. Then there is the data storage. As its name indicates, this one keeps a wide range of data. As for the governance and managementIt deals with integrated security controls and governance. Finally, we will highlight the data science tools. These are production-ready data elements from engineering to BI, AI and ML.

Focus on Databrick startup

The steps to configure Databricks are summarized in 7. Generally, Databricks proposes a free trial 14 days.

The first step is to search for “Databricks” on Google Cloud Platform Marketplace. Sign up for the free trial.

After starting the trial subscription, a link will be received from the Databricks menu item in Google Cloud Platform. This is the second step. It is to manage the configuration on the Databricks hosted account management page.

The next step is the creation of a space of work. This is the environment in Databricks to access the assets. This step requires an external web application that will be the control plan. We then move on to the fourth step. This creation requires three Kubernetes clusters of nodes in the Google Cloud Platform project.

It uses GKE to host the Databricks runtime, which is the data plan. The data always reside in the data plane (own data sources), not in the control plane. This distinction is then important. Then, for the fifth step, we have three choices. The creation of a table in Delta Lake is done either by uploading a file, connecting to supported data sources, or using a partner integration.

The sixth step is then to analyze the data. To do this, you need to create a Cluster Databricks. It is a combination of compute resources and configurations where jobs and notebooks run. Examples include Streaming Analytics, ETL Pipelines, Machine Learning and Ad-hoc analytics.

As for the seventh and final step, che cluster runtime is based on Apache Spark in these databricks. Most of the Databricks tools are based on open source technologies and libraries such as Delta Lake and MLflow.

What are the advantages of Databricks?

Databricks provides a platform for data analysis unified for data engineers, data scientists, data analysts and business analysts. It offers a large flexibility On different ecosystems – AWS, GCP, Azure.

In addition, the reliability and scalability of data via Delta Lake are ensured in Databricks.

It 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).

In addition, using MLFLOW, one can use AutoML and model lifecycle management. DataBricks has visualizations integrated basic visualizations. It also has a integration Github and bitbucket. In addition, hyperparameter setting is possible with the support of HYPEROPT.

It should be said that DataBricks is 10 times more fast than other ETLs. Its installation is not only simplebut it is also very easy to use.

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