Introduction to Databricks in 1 Hour

Databricks unifies data engineering, machine learning, and business intelligence on an elastic Apache Spark platform, enabling real-time processing, team collaboration, and built-in data quality checks with schema enforcement.

Databricks unifies data sources on a single platform to process, store, analyze, and model data from data lakes and data warehouses for business intelligence and artificial intelligence.

Databricks enables organizations to store, process, and analyze data across industries, powering real-time recommendations, inventory and supply chain analytics, fraud detection, and healthcare insights through AI and machine learning.

Explore how Databricks blends SaaS and PaaS, a cloud-native platform, to simplify data engineering, management, and analytics with core components like workspaces, notebooks, clusters, jobs, libraries, and data sources.

Delve into Databricks architecture and its key components in the Intro to Databricks course. Continue exploring the world of Databricks with this lesson.

Explore Databricks architecture, including the user interface, control plane, compute plane, storage layer, and security governance, plus three cluster types, Delta Lake, time travel, and Photon Engine.

Discover how Delta Lake enhances big data work with parquet files, a transaction log, and Delta tables that enable safe, fast updates, schema changes, and undo history for analytics.

Explore medallion architecture, layering data from bronze to silver to gold in a lakehouse, enabling analytics-ready data for BI tools like Power BI and Tableau.

Explore how Databricks Runtime powers data processing with a Spark-based software environment, offering standard and machine learning runtimes, built-in Delta Lake libraries, and three-year support for data exploration.

Explore running sql queries on Delta Lake within the Databricks lakehouse architecture. Learn classic, pro, serverless warehouses and how to build dashboards with SQL editors.

Discover Databricks editions: standard, premium, and enterprise, plus the free community edition; learn about production workspace, analytics, machine learning, 50x faster performance, and enterprise security with single sign-on.

Sign up for the Databricks free edition to access full platform features with serverless compute and trusted-domain restrictions, for educational use only.

Create and name a notebook in the Databricks workspace, then write and run Python code with intellisense, and use the diagnose error tool to fix coding mistakes.

Load a CSV file into a Databricks SQL table using Databricks storage, create a new schema, and run SQL queries with Genie Preview and show code to view generated SQL.

Learn to visualize data in Databricks SQL with simple clicks, building bar charts and other visuals like area, histogram, and heatmap, customized by campaign and channel using sum or count.

Explore how Databricks enables dashboards and visualizations from SQL data, using an AI assistant to create charts and refine visuals, including date difference calculations with the datediff function.

Create a Databricks dashboard, switch to a bar chart with campaign name on the axis, add channel and campaign name filters, and include a pivot chart of impressions by channel.

Learn to manipulate sql tables with python by loading sql tables into a spark dataframe, handling csv data issues, and computing duration in days using PySpark date_diff.

Use Python with PySpark SQL to clean a cost column by applying regex_replace to remove the dollar sign and convert the result to double, then display the dataframe to verify.

Explore the core data concept of the data frame in Python using Pandas, an Excel-like table of rows and columns with an index to preserve order.

Practice machine learning regression to predict tomorrow's Bitcoin price using historical data from CoinGecko. Download csv, upload to Databricks, create a table, and run regression in a notebook with Python.

Convert BTC_USD_YTD table into a spark dataframe, then a pandas dataframe, align and sort by date, drop missing values and total volume, and create a next-day price target for regression.

Train a linear regression model to predict tomorrow's bitcoin price using price and market cap. Evaluate with mean squared error on an 80/20 train-test split of training and unseen data.

Use pandas to reset the index, sort by date, and build a table with date, price, and predicted price, rounding difference and percentage error to two decimals for crypto research.

Import a Python notebook into Databricks by choosing import from file, browsing to the Bitcoin Sandbox file, and confirming import to load the course's notebook.