pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

The athena adapter plugin for dbt (https://getdbt.com)

Examples and custom spark images for working with the spark-on-k8s operator on AWS

🐋 Docker image for AWS Glue Spark/Python

Extract, transform, and load data for analytic processing using AWS Glue

This is a case study showing how to deploy "Wait-for-Callback" using Step Functions

a toolkit that provides an object-oriented interface for working with parquet datasets on AWS

Terraform configuration that creates several AWS services, uploads data in S3 and starts the Glue Crawler and Glue Job.

Pipeline ETL na AWS

Read the data from a source file using python and then produced that data to a kafka broker using a kafka producer , then consumed the message using a kafka consumer , uploaded the data to a aws s3 bucket then built crawler on top that and then queried that data using aws athena.

A pipeline within AWS to capture schema changes in S3 files and to update them in a DB.

ETL using application streaming and creating a Data Lake

AWS Kinesis Analytics gather metrics from various computers (cpu, memory), perform aggregation on Kinesis stream data using Kinesis Analytics (with flink) and store the stream data into AWS S3 bucket which is used by Amazon Athena for running various Analytics queries and rending charts using Grafana.

This workshop is to build a serverless data lake architecture using Amazon Kinesis Firehose for streaming data ingestion, AWS Glue for Data Integration (ETL, Catalogue Management), Amazon S3 for data lake storage, Amazon Athena for SQL big data analytics.

IaC (Terraform) of AWS Forecast pipeline using Glue as workflow manager

This Terraform module automates the setup of AWS Athena to query ALB access and connection logs stored in an S3 bucket.

1️⃣ Querying Parquet file from S3 using AwsWrangler. 2️⃣ Querying from Redshift tables using Glue & AwsWrangler

The Project aims to establish a robust data pipeline for tracking and analyzing sales performance using various AWS services. The process involves creating a DynamoDB database, implementing Change Data Capture (CDC), utilizing Kinesis streams, and finally, storing and querying the data in Amazon Athena.