Hands-on with Amazon Redshift

Describe the architecture of Amazon Redshift, highlighting the leader node and compute nodes. Examine how data ingestion flows directly to compute nodes and how parallel execution drives performance.

Explore data loading options for Amazon Redshift, optimize parallel loads across slices, and apply the copy command from S3 while using data distribution, slicing, and vacuuming concepts.

Explore data distribution concepts in Redshift, including even, key, and all distribution types, to maximize parallel execution, minimize cross-node data transfer, and co-locate join data for efficient star schema replication.

Explore practical redshift usage from basic syntax and expressions to core copy, ddl/dml commands, data types and functions, and user and group permissions.

Explore Amazon Redshift basic usage, focusing on primary keys, distribution keys, and sort keys (interleaved and compound), plus copy and insert commands, and essential analytics and table management.

Create an AWS account to spin up a redshift cluster and sign into the console. Set up Virginia region CloudWatch billing alarms for five and ten dollars.

Create a multi-node Amazon Redshift cluster, configure the VPC security group for external access, create an IAM user and credentials, and learn to stop, restart, and snapshot to control costs.

configure sql workbench to connect to your redshift cluster, enable auto commit, set up the redshift driver, and run basic queries with a keep-alive connection script.

Create and configure an S3 bucket, upload sample data, and load into Redshift, inspecting the five tables—date, customer, parts, supplier, and the line order fact table—for a data loading lab.

Load data into a Redshift cluster by configuring a data loading script with credentials, creating tables, and preparing copy commands for part 2 using S3 and sequel work bench.

Complete part two of the lab by loading data into a Redshift cluster with copy commands, debugging outputs, and monitoring large data loads.

Continue loading data into a Redshift cluster from S3, monitor progress, debug errors, and validate data with selects and distribution checks; then snapshot the cluster.

Practice querying a Redshift cluster, view the queries library, validate data with counts, and optimize queries using create table as select and distribution keys.

Optimize a Redshift cluster by choosing sort and distribution keys for joins, load large tables, and compare query benchmarks to reveal performance gains.

Master data loading best practices for Redshift, including staging to S3, splitting data, compression, copy and vacuum tuning, and debugging load errors.

Master data loading in Redshift with copy from S3, vacuuming, post-load verify, and analyze statistics. Tune WLM slots, monitor data skew across slices, and handle copy errors.

Learn to tune Amazon Redshift query performance through thoughtful table design, choosing sort and distribution keys, managing compression, and maintaining leader node statistics with vacuum and analyze updates.

Tune Redshift query performance by choosing optimal distribution keys and join keys in a star schema. Use compression, the copy command, and explain to benchmark and optimize your queries.

Connect Tableau Desktop to your Redshift cluster, configure credentials, and build joined tables (public schema, line order, and customer key) to create interactive visualizations for dashboards.

Learn to explore Redshift data with Tableau, create region and revenue visuals, calculate margins, drill down by nation, and save or refresh worksheets.

Explore how Redshift fits into enterprise data warehouses, from self-owned to public cloud solutions, and master basic and advanced usage, including tuning and optimization.