Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.

Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

This repository will help you to learn about databricks concept with the help of examples. It will include all the important topics which we need in our real life experience as a data engineer. We will be using pyspark & sparksql for the development. At the end of the course we also cover few case studies.

Simplified ETL process in Hadoop using Apache Spark. Has complete ETL pipeline for datalake. SparkSession extensions, DataFrame validation, Column extensions, SQL functions, and DataFrame transformations

A Data Platform built for AWS, powered by Kubernetes.

An IDE and translation engine for detection engineers and threat hunters. Be faster, write smarter, keep 100% privacy.

Python idiomatic SDK for Cortex™ Data Lake.

AWS Auto Terminate Idle AWS EMR Clusters Framework is an AWS based solution using AWS CloudWatch and AWS Lambda using a Python script that is using Boto3 to terminate AWS EMR clusters that have been idle for a specified period of time.

This solution helps you deploy ETL processes and data storage resources to create an Insurance Lake using Amazon S3 buckets for storage, AWS Glue for data transformation, and AWS CDK Pipelines. It is originally based on the AWS blog Deploy data lake ETL jobs using CDK Pipelines, and complements the InsuranceLake Infrastructure project

OEDI Data Lake Access

A library to accelerate ML and ETL pipeline by connecting all data sources

Built functional python ETL script with functions that initialized spark clusters using pyspark library to extract songs stored in S3 bucket. Partitioned songs data by year and artist_id and compressed in parquet output files to increase load performance. Used the overwrite mode in spark to ensure every new run of ELT script is overwritten in th…

This solution helps you deploy ETL processes and data storage resources to create an Insurance Lake using Amazon S3 buckets for storage, AWS Glue for data transformation, and AWS CDK Pipelines. It is originally based on the AWS blog Deploy data lake ETL jobs using CDK Pipelines, and complements the InsuranceLake ETL with CDK Pipelines project.

Udacity Data Engineer Nanodegree - Datalake Spark ETL

Udacity's Data Engineering Nanodegree project: Data Lake with Spark.

To implement a data lake using S3 and Spark on an EMR cluster using AWS Cloud9 environment and develop an ETL pipeline for a Data Lake that extracts data from S3, processes the data using Spark, and loads the data back into S3 as a set of dimensional tables.

Poor mans simple python api for creating a local or remote datalake based on several (pyarrow) datasets using duckdb

This is in order to add additional stocks data information using dbt.

pyspark streaming kafka(0.8.2) to hdfs

Repositório para armazenar códigos do projeto.