Complete Databricks & PySpark Bootcamp: Zero to Hero

Understand what Databricks is and why it differs from Apache Spark, a unified analytics platform built on Apache Spark. Examine data lakehouse capabilities, Delta Lake governance, and collaborative notebooks.

Databricks offers a fully managed, auto-scaling platform with lakehouse data governance, collaborative notebooks, and built-in automation, delivering faster processing and easy Power BI and Tableau integration over Spark alone.

Learn to create a Databricks free edition account, sign up with your email, verify, and explore the free edition UI with unity catalog and compute options.

Navigate the databricks free edition workspace, create and run notebooks with PySpark, Python, SQL, Scala, or R, and explore serverless compute, dashboards, and unity catalog.

learn how Databricks lakehouse unifies data lake and data warehouse in one system, enabling fast analytics, governance, and reliable transaction support on open formats.

Centralizes governance and data management for data and AI assets in Databricks, enabling data discovery, secure access, and auditing across workspaces.

Learn how to create and configure a Databricks catalog, set access privileges for workspaces and users, apply tagging, and prepare development and production catalogs.

Understand managed versus external tables in Databricks: managed tables store metadata and data within Databricks, while external tables store metadata in Databricks and data in cloud storage.

Discover volumes in Databricks as logical containers for unstructured and semi-structured data within the Unity Catalog, enabling centralized governance, targeted access control, and file discovery with manage and external volumes.

Create and manage volumes in the Unity Catalog, upload files into directories, and learn how dropping a manage volume deletes files while external volumes keep data.

Discover Spark as a general-purpose in-memory computation engine, compare it with MapReduce, learn in-memory processing, and how Spark SQL, MLlib, and multi-workload support boost fast analytics and developer productivity.

Understand spark architecture: a master-slave cluster with a driver, executors, and a cluster manager that allocates resources, converts code into jobs, and enables parallel processing across workers.

Master the resilient distributed data set (RDD) as spark's core data structure built on HDFS data, and learn how transformations and actions form a dag for in-memory, fault-tolerant processing.

Discover how a dag, a directed acyclic graph, represents Spark jobs as transformations before actions, illustrating lazy evaluation and how reading, filtering, and counting build the execution plan from rdds.

Kick off the PySpark practical by guiding you through reading files, creating a data frame, and applying PySpark transformations in Databricks, with setup of catalogs, databases, and volumes.

Learn how to read a CSV into a PySpark data frame using spark.read.csv or spark.read.format, set header and inferSchema, and display results with df.show or display df.

Create dataframes from json and parquet files in PySpark by reading json with multiline support or using spark.read.format, and load parquet data with spark.read.parquet.

Master the select transformation in PySpark to choose a subset of columns, preview with show, and alias names to simplify your dataframe when working with large parquet datasets.

Explore how to add and rename columns in PySpark using withColumn and withColumnRenamed, including creating derived columns from order id and setting default values.

Learn to filter PySpark dataframes with the filter function, using conditions like status, debit card, and starts with or ends with text patterns.

Learn how to sort data in PySpark using sort and order by, sorting by columns such as customer name or order id, with ascending or descending orders and handling nulls.

Learn how PySpark joins merge two dataframes on a common key, covering inner, left, right, full outer, and left semi and left anti joins.

Learn how to merge two data frames with the same schema using union and union all in PySpark, illustrated by employee data and practical Databricks examples.

Learn to handle null values in PySpark using fill and fillna to replace nulls with blank, zero, unknown, or other values, per column or across multiple columns.

Discover how the collect action retrieves all elements of a data set, best for smaller data after filter and group, using df.collect, while avoiding memory errors on large data.

Define a PySpark schema with struct type and struct field to set column names, data types, and nullable, then read a csv into a data frame using that schema.

Leverage pivot and unpivot in PySpark to convert row values into columns and vice versa, using group by and sum for aggregation, with practical examples on region, product, and quantity.

Learn to create and register a PySpark UDF to extend Spark SQL functions, reuse across DataFrames, and solve custom tasks like timestamp-to-date and age-group categorization—while noting UDFs’ cost.

Learn to create or replace temp view from a data frame in PySpark, turning it into a temporary, not permanent, session-scoped virtual table for SQL queries and data cleaning.

Master windows functions in PySpark, including row number, rank, dense rank, lag, and lead, using partition by and order by salary to rank salaries.

Partition by in PySpark splits large datasets into smaller files based on a region column to speed queries. Repartition adjusts in-memory or on-disk partitions and controls writes to parquet files.

Master date formatting in PySpark by using the date_format function to convert date or timestamp columns into any string format, with practical examples from dd mm yyyy to month names.

Discover PySpark date functions such as date add, date sub, date difference, year function, day of year, month function, day of month, current date, last day, next day, and quarter.

Explore how to use explode in PySpark to convert array columns into rows. See examples of explode, pos explode, and map expansions that yield positions and key-value pairs in dataframes.

Explore the Databricks files feature to create and manage .py and .txt files, share common variables via a single file, and read or write data using import and open.

learn how databricks lakehouse federation lets you query external data sources directly from sql or notebook without copying into delta tables, enabling cross-system analytics, unity catalog governance, and cost efficiency.

Connect to external data sources by creating a foreign connection and catalog, configure credentials, host, and catalog name, then query external tables via unity catalog.

Databricks SQL is a SQL-first workspace for business analysts to write queries, build dashboards, and connect to BI tools like Power BI and Tableau, with a fast, low-latency SQL warehouse.

Explore the complete Databricks & PySpark bootcamp's Databricks SQL overview: write queries in the SQL editor, switch catalogs and databases, run serverless queries, and export results.

Learn to use sql parameters in Databricks to filter by gender and continent, pass values at runtime, and use colon syntax with identifier for dynamic column names.

Create and reuse sql query snippets in a databricks sql editor to automate repetitive code, including static gender case and dynamic snippets with dollar pass values.

Discover how to write a complex Databricks SQL query using inner joins, a CTE, group by product, and row_number to identify top-selling products.

Learn to schedule a Databricks query, name the job for easy identification, and set a cadence from every minute to monthly, including time zone, server, and failure notifications.

Monitor and analyze every query using the query history, filter by user, time, and compute, and view query profiles to optimize transformations and memory usage.

Discover how the Databricks SQL cache stores first-time query results and serves cached data on later runs. Fetch from cache to boost performance and reduce costs.

Learn to create and manage alerts in Databricks SQL, configure notification destinations (email, Slack, or Microsoft Teams), and test conditions that trigger notifications.

Create visualizations and dashboards in databricks by choosing chart types, configuring x and y axes, applying filters, and publishing the dashboard for sharing.

Discover Genie in Databricks, an ai feature that converts natural language prompts into sql queries across tables. Learn to select data, review generated code, and obtain fast insights.

Learn how lakehouse jobs automate notebooks, SQL queries, and pipelines in Databricks, scheduling tasks at a set time and creating ETL-like pipelines with monitoring.

Create lake house pipeline that reads CSV from volume with a PySpark notebook and writes to delta table. Automate notebook and SQL steps in a lake house job.

Learn to build a lakehouse ETL pipeline in Databricks using an if-else task to conditionally run notebooks and SQL files based on date and weekday conditions.

Learn to automate Databricks workflows with a for each activity to run notebooks or sql files multiple times, controlled by if-else and optional concurrency in a pipeline.

master scheduling databricks notebooks with scheduled triggers, file arrival, and continuous streaming triggers, configuring frequency, time zone, and storage location to automate notebook runs.

Explore compute in Databricks by using a serverless warehouse or a dedicated cluster, configure driver and worker nodes, Spark version, and share clusters to run your jobs.

Set up a job notification to receive email alerts when a Databricks job succeeds, fails, or warns. Alerts can also route to Microsoft Teams, enabling immediate action after failures.

Learn to monitor Databricks jobs with run graphs, timelines, and lists; diagnose failures from error details and debug using job and pipeline ownership to track recent runs.

Discover how Spark Streaming enables real-time data processing, contrast batch and real-time workflows, and explore scenarios from Kafka data, IoT sensors, and fraud detection.

Learn how auto loader, a Databricks optimized source built on structured streaming, detects new cloud files, ingests them into delta tables, and handles schema changes at scale.

Learn how autoloader in Databricks automatically ingests new cloud storage files into a delta table, processing incrementally with schema evolution and checkpointing for scalable real-time analytics.

Set up a practical auto loader workflow in Databricks free edition. Create raw and destination folders, upload files, and load only new data into delta with checkpoint and schema.

Set up auto loader in Databricks by creating folder, catalog, database auto loader, and a branch volume with raw and destination directories and subfolders for new data and schema checkpoint.

Learn to implement autoloader with spark.readStream and cloudFiles for JSON in a raw folder, and manage schema evolution with rescue and add new column options, delta format, and checkpointing.

Use auto loader incremental load to process only new batch files, skipping already processed ones, as batch two uploads and writes only new data to the final destination.

Shows how auto loader handles schema evolution, adding a new payment method field and automatically incorporating it into rescue data using dictionary format, and demonstrates adding new columns.

Understand the copy into command in Databricks SQL, compare it with auto loader, and decide when batch loads suit modest volumes or ad hoc schedules.

Explore a practical copy into workflow using a delta table, auto loader, and a volume folder to load csv files incrementally, with header options and schema inference.

Explore Delta Live Tables in Databricks: declarative ETL/ELT pipelines via SQL or PySpark, with automatic orchestration, data quality checks, and Delta Lake ACID and time travel.

Identify how Delta Live Table solves complex pipeline orchestration by auto managing dependencies, enforces data quality rules, enables performance with auto scaling and photon engine, visual Dag lineage for monitoring.

Embrace the declarative dlt approach that minimizes orchestration and emphasizes transformation logic. Leverage unified batch and streaming, data quality rules, lineage, monitoring, auto scaling, and medallion architecture.

Learn delta live tables use cases, including a medallion architecture with silver and gold layers, unified streaming plus batch pipelines, and automated data quality, self-healing, and ml feature engineering.

Explore building a DLT ETL pipeline in the Databricks free edition, configuring catalogs and a serverless compute environment with SQL or Python, then schedule, run, and dry-run pipelines.

Understand how DLT uses streaming tables for real-time data as it arrives, and how materialized views store results in delta format with incremental refresh, while views provide intermediate logic.

Execute a DLT practical in Databricks to create a streaming table via an ETL pipeline using a JSON data location, then dry run and run to create branch raw orders.

Create a materialized view for the silver layer from the order table, filtering status = completed and selecting key fields. Use a dry run to validate syntax and observe DAG.

Create a temporary view from the silver table to filter India orders, validate syntax with a dry run, and observe automatic dependency resolution and materialized views in the medallion architecture.

Learn to create a DLT streaming table, a materialized view, and a view using Python in Delta Live Tables, switching from SQL to Python.