Get Certified in Azure Databricks with Our Training

Getting certified in Azure Databricks can open doors to new career opportunities and boost your earning potential.

Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform that's optimized for Microsoft Azure.

To get certified, you'll need to have a good understanding of data engineering, data science, and data analytics concepts.

Our training program is designed to equip you with the skills and knowledge you need to pass the Azure Databricks certification exam.

You'll want to get certified in Azure Databricks because it shows employers you have data engineering skills. This certification is a great way to stand out in a competitive job market.

The certification is offered after completing a training course, and you'll receive an electronic course completion certificate from Techsolidiy. You can share this certificate on social media platforms to showcase your skills.

Having this certification can give you a competitive edge when applying for data engineering jobs. It's a tangible way to demonstrate your expertise in Azure Databricks.

The Azure Databricks certification course is designed to transform learners into skilled Databricks professionals.

You can expect to cover a wide range of topics, including cloud technology, big data, and Databricks itself. The course starts with the basics of cloud technology, covering what it is, its need, and the different types of cloud services.

The course then delves into big data, introducing you to its core elements and types. You'll learn about the different types of big data, which is essential for working with Databricks.

One of the key areas of focus is on Databricks itself, covering what it is, its introduction, and how to create an account. You'll also learn about the detailed architecture of Databricks and how to navigate its web UI.

In terms of practical skills, the course covers cluster creation and configuration, including cluster types, auto scaling, and multi-user clusters. You'll also learn about Databricks pools and clusters modes.

Here's a breakdown of the key topics you can expect to cover in the Azure Databricks certification course:

To get started with Azure Databricks, you'll need to have a basic understanding of Python and SQL. This is a prerequisite for our Microsoft Azure Databricks training, which covers all the essential areas such as cloud basics, Azure Cloud, Databricks, Notebooks, PySpark, and more.

The core agenda of this Databricks training is to infuse learners with the right data engineering skills. You'll gain all the skills required to ingest data from multiple sources, clean, store, share, analyze, and model massive volumes of data sets. This includes learning about data frames, structured streaming, and more.

To join our Microsoft Azure Databricks training, you should have a basic understanding of Python and SQL. Having knowledge of the cloud would also be helpful. Our training program covers all the essential areas, including cloud basics, Azure Cloud, Databricks, and more.

Here are some key skills you'll learn in our Azure Databricks training:

Upon completing the Azure Databricks certification course, you'll gain a clear approach to using Azure Databricks.

You'll be able to integrate Databricks with different Azure components, such as Azure Data Lake Gen2 and Azure Data Factory (ADF).

The course covers transforming data using Spark SQL in Azure Databricks, which is a powerful tool for data analysis.

You'll learn to build Data Factory Pipelines to work with Notebooks, allowing you to automate data processing tasks.

The training program also focuses on data engineering skills, including Delta Lake, Spark Core, and Azure Data Factory (ADF).

Able to connect, ingest, and transform data using PySpark in Databricks, a popular Python library for data processing.

Here's a summary of the key skills you'll gain from the course:

Azure Databricks can integrate with various Azure services to enhance data processing and analytics.

You can mount Azure Data Lake Storage Gen1 to DBFS, allowing for seamless access to your data. This is a crucial step in reading and writing files from Gen1.

One of the most popular integrations is with Azure SQL Database, which enables you to query and analyze data stored in SQL databases directly from Databricks.

Azure Databricks can also integrate with Azure Synapse, Snowflake, and CosmosDB SQL API, expanding your data processing capabilities.

Here are some of the key integrations you can leverage:

By integrating with these services, you can unlock new insights and optimize your data workflows.

Data Handling and Analytics is a crucial aspect of Azure Databricks certification. To master this, you need to understand how to collect and process large datasets efficiently.

Collecting data is just the first step, and you'll want to use methods like Collect() and InferSchema to get started. Built-in Spark functions like those found in Pandas and Koalas can also help.

For more advanced data handling, you'll want to learn about Spark Partitioning Methods and Bucketing in Spark. These techniques can significantly improve the performance of your data processing tasks.

Here are some key concepts to keep in mind:

To work with data in Azure Databricks, you'll need to understand how to create and interact with DataFrames. This includes creating DataFrames, performing DataFrame transformations, and executing DataFrame actions.

File handling is a crucial aspect of data handling and analytics. It involves managing and processing various types of files to extract valuable insights.

Databricks supports handling of multiple file formats, including CSV, Excel, and Parquet files. These formats are widely used in data analysis and can be easily integrated with Databricks.

Databricks is also capable of handling complex JSON files, which are often used in large-scale data storage and processing. This feature allows users to work with diverse data sources.

The types of file handling supported by Databricks include:

This versatility in file handling enables users to work with a wide range of data sources, making Databricks a powerful tool for data analysis and analytics.

Big Data Analytics is a crucial aspect of data handling and analytics. It involves collecting and processing large datasets to gain valuable insights.

The Collect() Method is a key part of this process, allowing you to gather and store data from various sources. It's a fundamental concept in data analytics.

InferSchema is a feature that automatically detects the schema of your data, making it easier to work with. This can save you a significant amount of time and effort.

Built-in-Spark functions, such as those found in Pandas and Koalas, provide a range of tools for data manipulation and analysis. These libraries are designed to work seamlessly with Spark.

Spark Partitioning Methods, including bucketing, are essential for optimizing data processing. By dividing your data into smaller chunks, you can improve performance and reduce processing times.

Spark SQL optimizations, such as those mentioned in the article, can also help improve query performance. By using techniques like caching and indexing, you can speed up data retrieval and analysis.

Here are some key concepts to keep in mind when working with Big Data Analytics:

Data Frames are a fundamental concept in data handling and analytics. They allow us to store and manipulate large datasets with ease.

To create a DataFrame, you can use the Collect() Method, which is a built-in feature in Spark. This method helps you collect data from various sources and store it in a DataFrame.

DataFrames can be interacted with using various methods, including InferSchema, which automatically detects the schema of the data. You can also use built-in-Spark functions, such as Pandas and Koalas, to perform various operations on the DataFrame.

One of the key features of DataFrames is the ability to perform transformations on the data. This can be achieved using DataFrame Transformations, which include methods like filtering, sorting, and grouping.

Another important aspect of DataFrames is the ability to perform actions on the data. This can be achieved using DataFrame Actions, which include methods like writing the data to a file or database.

Here are some key methods for working with DataFrames:

DataFrames can be used in various scenarios, including data analysis, machine learning, and data visualization. By mastering DataFrames, you can unlock the full potential of your data and gain valuable insights into your business or organization.