All about DATA

Databases & APIs

These are the primary sources from where data is extracted.

Relational Databases (SQL)

MySQL
PostgreSQL
Microsoft SQL Server
Oracle Database
IBM Db2
MariaDB
Amazon RDS (Relational Database Service)

NoSQL Databases

MongoDB (Document-based)
Cassandra (Columnar store)
Redis (Key-value store)
DynamoDB (AWS NoSQL)
CouchDB
Elasticsearch (Search-based database)

Cloud & Big Data Storage

Google Cloud Storage
Amazon S3
Azure Blob Storage
HDFS (Hadoop Distributed File System)

APIs & Streaming Sources

REST & SOAP APIs
Apache Kafka (Real-time data streaming)
Apache Pulsar
Amazon Kinesis
Google Pub/Sub
Web Scraping (BeautifulSoup, Scrapy, Selenium)

Data Files

1. Structured Data Files

These files store data in a structured format (rows and columns).

CSV (Comma-Separated Values) → Common for tabular data, but lacks schema enforcement.
TSV (Tab-Separated Values) → Similar to CSV but uses tab as a separator.
XLS/XLSX (Excel Sheets) → Used in business applications.

2. Semi-Structured Data Files

These files have some structure but are not strictly relational.

JSON & XML → Often used in NoSQL databases (MongoDB, Cassandra).
YAML → Configuration files in DevOps, data pipelines.
Avro (Apache Avro) → Binary format with schema evolution (used in Kafka, Big Data).
Parquet (Apache Parquet) → Optimized for analytical queries (columnar format).
ORC (Optimized Row Columnar) → Used in Hive & Spark, optimized for performance.

3. Unstructured Data Files

These files do not have a predefined format or schema.

Text Files (.txt, .log) → Used for logs, raw data dumps.
PDFs & Word Documents → Reports, documents with embedded data.
Images (JPEG, PNG, TIFF, BMP) → Used in AI/ML and computer vision projects.
Audio Files (MP3, WAV, FLAC) → Speech recognition, media analytics.
Video Files (MP4, AVI, MKV) → Video processing in analytics.

4. Log & Event Files

These files store system and application logs.

Log Files (.log, .txt) → System logs, error logs, debugging.
Syslog (System Log Format) → Used in Linux/Unix logging.
Web Server Logs (Apache, Nginx) → Captures HTTP requests and user interactions.
Kafka Topics (Streaming Data Logs) → Used in real-time streaming systems.

5. Compressed Files

These files store large datasets efficiently.

ZIP, GZIP, TAR, 7z → Used to compress large data files.
Snappy, BZIP2, LZ4 → Used for Big Data storage (Hadoop, Spark).

ETL Tools

Non-Cloud Tools

Informatica PowerCenter – A leading ETL tool for enterprise data integration.
IBM DataStage – Part of the IBM InfoSphere suite, used for large-scale ETL processing.
Microsoft SQL Server Integration Services (SSIS) – Microsoft’s ETL tool for SQL Server-based workflows.
Oracle Data Integrator (ODI) – Oracle’s powerful ETL and data integration solution.
SAP Data Services – ETL and data transformation tool from SAP for enterprise applications.

Cloud ETL/ELT tools

AWS Glue – A fully managed, serverless ETL service optimized for AWS data lakes and Redshift.
Google Cloud Dataflow – A real-time, serverless ETL tool based on Apache Beam for streaming and batch data processing.
Azure Data Factory – A cloud ETL service that enables data movement, transformation, and orchestration within the Azure ecosystem.
Matillion – A cloud-native ETL tool designed for Snowflake, Redshift, BigQuery, and other cloud data warehouses.
Fivetran – A fully automated ELT solution with pre-built connectors for quick data pipeline setup across various platforms.

DatawareHouses

Snowflake – A cloud-based, fully managed data warehouse with auto-scaling and pay-as-you-use pricing.
Google BigQuery – A serverless, highly scalable, and cost-effective data warehouse for analytics on Google Cloud.
Amazon Redshift – A fast, petabyte-scale data warehouse on AWS with columnar storage for optimized queries.
Azure Synapse Analytics – A cloud data warehouse by Microsoft that integrates with SQL, Spark, and Power BI.
Databricks Lakehouse – A hybrid of a data lake and warehouse, optimized for AI and big data workloads.
Teradata Vantage – A high-performance analytics platform designed for large-scale enterprise data processing.
IBM Db2 Warehouse – A cloud or on-premises data warehouse optimized for AI and machine learning.
Oracle Autonomous Data Warehouse – A self-driving, self-securing, and self-repairing data warehouse by Oracle.
SAP Data Warehouse Cloud – A cloud-based warehouse integrated with SAP applications and analytics.
Vertica – A high-speed, columnar data warehouse optimized for big data analytics.

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