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.

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

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.

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.

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 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 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.

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 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.

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.

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.

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.

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.

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

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.

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 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.

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.

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.

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.