Data Engineering & Analytics
Courses on data science, data lakes, ETL, data warehousing, NoSQL databases, and the lakehouse.
Tutorials
Databricks Module 8: SQL Warehouses, Performance, Cost, and a First Look at MLflow
Serve analysts with Databricks SQL, tune Spark and Delta performance, control cost, and track a model with MLflow.
Databricks Module 7: Jobs, Orchestration, and Deploying with Git and Bundles
Schedule multi-task workflows with Lakeflow Jobs, pass parameters, handle failures, and deploy code with Git and Asset Bundles.
Databricks Module 6: Declarative Pipelines and the Medallion Architecture
Build bronze, silver, and gold tables declaratively with Lakeflow Spark Declarative Pipelines, including data quality expectations.
Databricks Module 5: Ingesting Files with Auto Loader and COPY INTO
Load new files incrementally with Auto Loader, handle schema evolution, and use COPY INTO for simple batch loads.
Databricks Module 4: Unity Catalog, Volumes, and Governance
Organize data in catalogs and schemas, store files in volumes, grant permissions, and use lineage.
Databricks Module 3: Delta Lake Tables, MERGE, and Time Travel
Create Delta tables, update data with MERGE, query earlier versions, and keep tables healthy with OPTIMIZE and VACUUM.
Databricks Module 2: Notebooks and Spark DataFrames with PySpark
Create DataFrames, select, filter, aggregate, join, and use Spark SQL in Databricks notebooks.
Databricks Module 1: The Lakehouse Platform and Your First Notebook
Understand what Databricks is, how its workspace and compute fit together, and run your first notebook on Free Edition or a trial workspace.
MongoDB Module 8: Security, Atlas Features, and Using MongoDB from Code
Secure a deployment, learn Atlas Search and Vector Search, and connect from Python and Node.js.
MongoDB Module 7: Transactions, Replication, and Sharding
Understand replica sets, read and write concerns, multi-document transactions, change streams, and how sharding scales a cluster.
MongoDB Module 6: Indexes and Query Performance
Create single, compound, and TTL indexes, read explain plans, and follow the equality, sort, range rule.
MongoDB Module 5: Schema Design and Validation
Decide when to embed and when to reference, use common design patterns, and enforce structure with JSON Schema validation.
MongoDB Module 4: The Aggregation Framework
Build pipelines with $match, $group, $project, $unwind, $lookup, and $sort to analyze data inside MongoDB.
MongoDB Module 3: Query Operators, Arrays, Nested Fields, and Pagination
Write richer queries with comparison and logical operators, query nested fields and arrays, and paginate results.
MongoDB Module 2: CRUD Operations
Insert, read, update, and delete documents with mongosh, including update operators, projections, and upserts.
MongoDB Module 1: Introduction, the Document Model, and Setup
Learn what MongoDB is, how documents differ from tables, and set up MongoDB with Docker or Atlas and connect with mongosh.
Amazon Redshift Module 8: Operations, Cost, and a Capstone Project
Back up and restore, monitor and control cost, and finish by building a small analytics warehouse end to end.
Amazon Redshift Module 7: Security, Data Sharing, and Integrations
Control access with roles and fine-grained permissions, share live data between warehouses, and understand zero-ETL integrations.
Amazon Redshift Module 6: Redshift Spectrum and Querying the Data Lake
Query data that stays in Amazon S3 using external schemas and the Glue Data Catalog, and combine it with warehouse tables.
Amazon Redshift Module 5: Performance Tuning and Maintenance
Read query plans, find slow queries, understand caching and concurrency scaling, and keep tables healthy.
Amazon Redshift Module 4: Querying, Window Functions, and Semi-Structured Data
Write analytical SQL with CTEs and window functions, and query JSON with the SUPER data type.
Amazon Redshift Module 3: Loading Data with COPY and Exporting with UNLOAD
Load files from S3 into Redshift with COPY, troubleshoot load errors, and export query results with UNLOAD.
Amazon Redshift Module 2: Designing Tables with Distribution Keys and Sort Keys
Create tables with the right data types, distribution styles, sort keys, and compression for fast queries.
Amazon Redshift Module 1: Architecture and Setting Up Serverless
Understand how Redshift stores and queries data, choose between Serverless and provisioned, and create a workgroup with cost limits.
AWS Glue Module 8: Monitoring, Performance, Data Quality, and Cost
Monitor jobs, tune workers and auto scaling, add Glue Data Quality rules, and keep Glue costs under control.
AWS Glue Module 7: Triggers, Workflows, and Job Parameters
Schedule and chain crawlers and jobs, pass parameters, and handle failures and retries.
AWS Glue Module 6: Partitions, Pushdown Predicates, and Job Bookmarks
Process only new or needed data with partition filters and job bookmarks, and write well-partitioned output.
AWS Glue Module 5: Transforms and Cleaning Data
Use ApplyMapping, ResolveChoice, DropNullFields, Filter, and Join, and clean data with Spark functions.
AWS Glue Module 4: Writing PySpark Scripts with GlueContext and DynamicFrames
Write a Glue ETL script by hand: read from the catalog, transform with DynamicFrames, and write partitioned Parquet.
AWS Glue Module 3: Your First ETL Job with Glue Studio
Build a visual ETL job that reads the cataloged table, filters and renames columns, and writes Parquet to S3.
AWS Glue Module 2: The Data Catalog and Crawlers
Create a catalog database, run a crawler over S3 data, and understand how tables, schemas, and partitions are recorded.
AWS Glue Module 1: What Glue Is and Setting It Up
Learn the parts of AWS Glue, how it is priced, and create the IAM role and S3 sample data you need for the course.
Building a Data Lake Module 7: Governance, Security, Data Quality, and Cost
Protect data with IAM and Lake Formation, add quality checks and lineage, and keep the lake affordable.
Building a Data Lake Module 6: Open Table Formats with Iceberg, Delta Lake, and Hudi
Add transactions, updates, deletes, and time travel to lake data with Apache Iceberg, Delta Lake, or Apache Hudi.
Building a Data Lake Module 5: Cataloging and Querying with Glue and Athena
Register data in the AWS Glue Data Catalog and query it in place with SQL in Amazon Athena.
Building a Data Lake Module 4: Ingesting Data into the Lake
Bring in data from files, databases, applications, and streams, and make ingestion repeatable and safe to rerun.
Building a Data Lake Module 3: File Formats, Compression, and Partitioning
Choose between CSV, JSON, Parquet, and Avro, and design partitions that make queries fast and cheap.
Building a Data Lake Module 2: Storage Foundations on Amazon S3
Create buckets and a clean folder structure, and turn on encryption, versioning, and lifecycle rules.
Building a Data Lake Module 1: What a Data Lake Is and How It Is Organized
Understand data lakes, warehouses, and lakehouses, and learn the raw, clean, and curated layer design.
Data Science with Python Module 8: From Notebook to a Finished Project
Structure a project, save and reload a model, write a prediction function, and communicate results clearly.
Data Science with Python Module 7: Evaluating and Improving Models
Choose the right metrics, use cross-validation, tune hyperparameters, and avoid data leakage and overfitting.
Data Science with Python Module 6: Machine Learning with scikit-learn
Train and test a regression model and a classifier with scikit-learn, and combine preprocessing and a model in a pipeline.
Data Science with Python Module 5: Statistics for Data Science
Learn distributions, sampling, confidence intervals, and hypothesis tests, and run them with SciPy.
Data Science with Python Module 4: Exploratory Data Analysis and Visualization
Summarize a dataset, look at distributions and relationships, and draw clear charts with matplotlib.
Data Science with Python Module 3: Loading and Cleaning Data
Read files into pandas, then fix missing values, wrong types, duplicates, inconsistent text, and outliers.
Data Science with Python Module 2: NumPy and pandas Basics
Work with arrays in NumPy and tables in pandas: select, filter, sort, and summarize data with groupby.
Data Science with Python Module 1: What Data Scientists Do and Setting Up Python
Understand the data science workflow, then set up Python, a virtual environment, and Jupyter with the core libraries.