Data Engineering
About This Document
Gain a comprehensive overview of the diverse software development tools and technologies utilized by Growth Acceleration Partners (GAP) in various domains. This document covers UI/UX design, cloud-native platforms, legacy applications, back-end API development, mobile app development, software quality engineering, web development, front-end development, and platform engineering. It lists specific tools and programming languages used in these areas, showcasing the wide array of technologies that developers can leverage to create, test, and deploy applications effectively. This detailed guide assists in selecting the appropriate tools and technologies based on project requirements in software development.
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Data Engineering
- SQL
- MongoDB
- AWS DynamoDB
- AWS S3
- Azure Data Lake
- Azure Cosmos
- SSIS
- SQL Server
- Pentaho
Data Science
- Python
- R
- Jupyter
- Pandas
- Tensorflow
- Pytorch
- AWS SageMaker
- Transformers
- H20
- Langchain
Data as a Product
- Snowflake
- Databricks
- Hadoop
- Spark
- AWS Redshift
- AWS Kinesis
- AWS Glue
Machine Learning & AI
- Python
- R
- Jupyter
- Pandas
- Tensorflow
- Pytorch
- AWS SageMaker
- Transformers
- H20
- Langchain
Large Language Models
- Python
- R
- Jupyter
- Pandas
- Tensorflow
- Pytorch
- AWS SageMaker
- Transformers
- H20
- Langchain
Business Intelligence
- PowerBI
- Tableau
- AWS Quicksight
- Looker Studio
Quality Engineering
- Jira
- SQL
- Postman
- Browserstack
- Saucelabs
- TestRail
- Zephyr
IoT & Connected Devices
- Particle IoT Platform
- AWS IoT
- Azure IoT
- Node-red IoT
- iBeacon
- Zigbee
- LoRaWAN
- MQTT
- BLE