Is this course good for beginners?

Yes, Databricks Certification Training is beginner-friendly and teaches everything from the basics.

Do I need to know programming before starting?

Basic Python or SQL helps, but we’ll cover what you need during training.

Will I get a certificate after this course?

Yes, You will get a completion certificate. You can also take official Databricks certification exams.

Is the course 100% practical?

Yes, the course is focused on hands-on training with real data projects.

Will I get job assistance after the course?

Yes, we offer full placement support including interview prep and job leads.

Home /Cloud Computing/ Databricks Training Program

Master Databricks for Data Engineering, Analytics, and AI with Expert-Led Training.

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Course Duration

40 Hrs.

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Databricks training allows you to get trained in big data work, data pipelines construction, and doing data analysis and machine learning with the help of the Databricks platform. You learn by working on live projects and through working on applications such as Apache Spark, SQL, and Python. Databricks training is meant for anyone who desires to be a data engineer, an analyst, or a data scientist. You will also prepare to get certified at Databricks. At the end of the course, You will be prepared to work in businesses that handle a lot of data and desire qualified professionals to handle it.

Databricks Online Course is meant to guide you through how to work with data using Databricks. It focuses on hands-on tasks like data pipeline creation, cleaning data, and running analysis with Spark and Python.
What You Will Learn:

Understand how the Databricks Lake House integrates the best of data lakes and warehouses.

Learn how to perform ETL operations (Extract, Transform, Load) with Spark and SQL/Python.

Construct data pipelines that can process large volumes of data.

Understand data security and quality to secure your data and keep it clean.

Prepare to pass official Databricks certification exams.

If youre a fresher just starting your career, Databricks Online Training can help you land a good job with a great salary in the data field.
How Much You Can Earn:

In India: Freshers can earn between 6 to 10 LPA.

In the US: Freshers earn about $84,000 per year.

Completion of this course gives you a lot of opportunities for growth and development as an individual and a professional. You will start off with entry-level positions, which will be a good building block for your career. With experience and a superior skill set, you can move to more sophisticated jobs with greater responsibility and pay.
Career Path Options:

Junior Data Engineer Start by helping build and manage data pipelines.

Data Analyst Work on reports and business insights.

Machine Learning Engineer Train and use AI models.

Senior Data Engineer Lead data projects and guide a team.

Data Architect Plan and build entire data systems.

If we speak about the course popularity there are numerous. Much data can be processed and quickly analyzed with the help of Databricks. Students can gain valuable skills to search for a job in this course, which is much desired as it offers them hands-on experience.
Why Everyone Wants This Course:

Big Demand: Many companies need Databricks experts.

Practical Learning: You learn by doing real projects.

Certifications: You prepare for industry-level certifications.

Used Worldwide: Databricks is used in tech, banking, healthcare, and more.

Upon completing the Databricks Certification Course, you will be skilled enough to apply for jobs in big data administration, data analysis, and machine learning model creation. The course equips you with a solid grasp of working with huge datasets, unraveling insights using analytical techniques, and implementing complex algorithms to develop forecasting models. This certification will equip you sufficiently to tackle complex data issues and contribute meaningfully to data-driven projects.
Job Roles You Can Apply For:

Data Engineer Builds systems to move and clean data.

Data Analyst Studies data to give useful business info.

Machine Learning Engineer Creates and uses AI models.

Data Scientist Works with complex data and makes predictions.

Data Architect Designs how data should flow and be stored.

What You will Do on the Job:

Use Apache Spark to process data fast.

Create pipelines to move and clean data.

Work in teams with engineers, analysts, and developers.

Keep data systems running smoothly and securely.

Databricks skills are needed in every industry that works with large amounts of data.
Who Hires Databricks Experts:

Tech Companies Like Microsoft, Amazon, Google.

Banks & Finance To study data for business decisions.

Healthcare For managing patient data and reports.

Retail To track sales and inventory trends.

Consulting Firms That help other companies manage data.

After completing the course, You will get a certificate. You can also take Databricks exams to get official certificates, which help you get jobs faster.
Popular Databricks Certificates:

Certified Spark Developer For Spark programming.

Certified Data Engineer Associate For building data pipelines.

Certified Machine Learning Associate For ML-related work.

We help our students find the right job after completing the course. Our placement support includes:

Resume writing and profile building.

Practice interviews and mock tests.

Access to our network of hiring partners.

One-on-one mentoring sessions.

Job alerts and interview scheduling help.

We also connect you with alumni already working in companies using Databricks.

Why Should You Learn Databricks Online Course?

Enjoy higher salaries as a skilled Databricks professional.

Get a chance to work with leading industries worldwide.

There is a HUGE demand for skilled Databricks professionals.

Enjoy higher salaries as a skilled Databricks professional.

Get a chance to work with leading industries worldwide.

There is a HUGE demand for skilled Databricks professionals.

Enjoy higher salaries as a skilled Databricks professional.

Not just learning –

we train you to get hired.

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CURRICULUM & PROJECTS

Databricks Training Program

Describe the relationship between the data lakehouse and the data warehouse.

Identify the improvement in data quality in the data lakehouse over the data lake.

Compare and contrast silver and gold tables, which workloads will use a bronze table as a source, which workloads will use a gold table as a source.

Identify elements of the Databricks Platform Architecture, such as what is located in the

data plane versus the control plane and what resides in the customer’s cloud account

Differentiate between all-purpose clusters and jobs clusters.

Identify how cluster software is versioned using the Databricks Runtime.

Identify how clusters can be filtered to view those that are accessible by the user.

Describe how clusters are terminated and the impact of terminating a cluster.

Identify a scenario in which restarting the cluster will be useful.

Describe how to use multiple languages within the same notebook.

Identify how to run one notebook from within another notebook.

Identify how notebooks can be shared with others.

Describe how Databricks Repos enables CI/CD workflows in Databricks.

Identify Git operations available via Databricks Repos.

Identify limitations in Databricks Notebooks version control functionality relative to Repos.

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Extract data from a single file and from a directory of files

Identify the prefix included after the FROM keyword as the data type.

Create a view, a temporary view, and a CTE as a reference to a file

Identify that tables from external sources are not Delta Lake tables.

Create a table from a JDBC connection and from an external CSV file

Identify how the count_if function and the count where x is null can be used

Identify how the count(row) skips NULL values.

Deduplicate rows from an existing Delta Lake table.

Create a new table from an existing table while removing duplicate rows.

Deduplicate a row based on specific columns.

Validate that the primary key is unique across all rows.

Validate that a field is associated with just one unique value in another field.

Validate that a value is not present in a specific field.

Cast a column to a timestamp.

Extract calendar data from a timestamp.

Extract a specific pattern from an existing string column.

Utilize the dot syntax to extract nested data fields.

Identify the benefits of using array functions.

Parse JSON strings into structs.

Identify which result will be returned based on a join query.

Identify a scenario to use the explode function versus the flatten function

Identify the PIVOT clause as a way to convert data from wide format to a long format.

Define a SQL UDF.

Identify the location of a function.

Describe the security model for sharing SQL UDFs.

Use CASE/WHEN in SQL code.

Leverage CASE/WHEN for custom control flow.

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Identify where Delta Lake provides ACID transactions

Identify the benefits of ACID transactions.

Identify whether a transaction is ACID-compliant.

Compare and contrast data and metadata.

Compare and contrast managed and external tables.

Identify a scenario to use an external table.

Create a managed table.

Identify the location of a table.

Inspect the directory structure of Delta Lake files.

Identify who has written previous versions of a table.

Review a history of table transactions.

Roll back a table to a previous version.

Identify that a table can be rolled back to a previous version.

Query a specific version of a table.

Identify why Zordering is beneficial to Delta Lake tables.

Identify how vacuum commits deletes.

Identify the kind of files Optimize compacts.

Identify CTAS as a solution.

Create a generated column.

Add a table comment.

Use CREATE OR REPLACE TABLE and INSERT OVERWRITE

Compare and contrast CREATE OR REPLACE TABLE and INSERT OVERWRITE

Identify a scenario in which MERGE should be used.

Identify MERGE as a command to deduplicate data upon writing.

Describe the benefits of the MERGE command.

Identify why a COPY INTO statement is not duplicating data in the target table.

Identify a scenario in which COPY INTO should be used.

Use COPY INTO to insert data.

Identify the components necessary to create a new DLT pipeline.

Identify the purpose of the target and of the notebook libraries in creating a pipeline.

Compare and contrast triggered and continuous pipelines in terms of cost and latency

Identify which source location is utilizing Auto Loader.

Identify a scenario in which Auto Loader is beneficial.

Identify why Auto Loader has inferred all data to be STRING from a JSON source

Identify the default behavior of a constraint violation

Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATEfor a

constraint violation

Explain change data capture and the behavior of APPLY CHANGES INTO

Query the events log to get metrics, perform audit loggin, examine lineage.

Troubleshoot DLT syntax: Identify which notebook in a DLT pipeline produced an error,

identify the need for LIVE in create statement, identify the need for STREAM in from clause.

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Identify the benefits of using multiple tasks in Jobs.

Set up a predecessor task in Jobs.

Identify a scenario in which a predecessor task should be set up.

Review a task's execution history.

Identify CRON as a scheduling opportunity.

Debug a failed task.

Set up a retry policy in case of failure.

Create an alert in the case of a failed task.

Identify that an alert can be sent via email.

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Identify one of the four areas of data governance.

Compare and contrast metastores and catalogs.

Identify Unity Catalog securables.

Define a service principal.

Identify the cluster security modes compatible with Unity Catalog.

Create a UC-enabled all-purpose cluster.

Create a DBSQL warehouse.

Identify how to query a three-layer namespace.

Implement data object access control

Identify colocating metastores with a workspace as best practice.

Identify using service principals for connections as best practice.

Identify the segregation of business units across catalog as best practice.

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Course Duration

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