Paid Tools & Datasets for Python Data Engineering

Amazon Simple Storage Service offers industry-leading scalability, data availability, security, and performance for object storage. Commonly used for data backup, archival, big data analytics, disaster recovery, and content distribution. Provides 99.999999999% durability and integrates seamlessly with AWS analytics and ML services.

Amazon Elastic Compute Cloud provides secure, resizable compute capacity in the cloud. Offers wide selection of instance types optimized for different use cases including compute-intensive, memory-intensive, and storage-optimized workloads. Perfect for running data processing jobs, ML training, and distributed applications.

Fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to analyze all your data using standard SQL and existing BI tools. Offers fast query performance using columnar storage, data compression, and massively parallel query execution. Integrates with AWS data lake and analytics services.

Microsoft's object storage solution for the cloud, optimized for storing massive amounts of unstructured data. Offers hot, cool, and archive access tiers for cost optimization. Ideal for serving images, documents, streaming video and audio, data lakes, backup and disaster recovery, and big data analytics.

Scalable and secure data lake that enables high-performance analytics workloads. Built on Azure Blob Storage with hierarchical namespace capabilities. Integrates seamlessly with Azure analytics services like Synapse, Databricks, and HDInsight. Optimized for big data analytics with enterprise-grade security and compliance.

Analytics service that brings together enterprise data warehousing and Big Data analytics. Provides unified experience to ingest, explore, prepare, manage, and serve data for immediate BI and machine learning needs. Supports both serverless and dedicated resource models with deep integration with Power BI and Azure ML.

Unified object storage for developers and enterprises, from live applications data to cloud archival. Offers multiple storage classes including Standard, Nearline, Coldline, and Archive for cost optimization. Provides strong consistency, high durability, and seamless integration with Google Cloud data analytics and ML services.

Offers virtual machines running in Google's innovative data centers and worldwide fiber network. Provides predefined and custom machine types, sustained use discounts, and per-second billing. Ideal for compute-intensive workloads, batch processing, and running distributed data processing frameworks like Spark and Hadoop.

Fast, economical, and fully managed serverless data warehouse for large-scale data analytics. Enables super-fast SQL queries using the processing power of Google's infrastructure. Built-in machine learning capabilities, automatic scaling, and pay-per-query pricing. Ideal for analyzing petabytes of data with standard SQL.

Powerful enterprise data modeling tool that enables companies to document critical data assets, design databases, and enforce data governance across the organization. Supports various database systems and data warehouses. Ideal for large teams requiring advanced modeling capabilities and data consistency enforcement.

Enterprise data cloud offering storage, processing, and exploration capabilities for any data. Focuses on enterprise-level data management and analytics with comprehensive support for Hadoop ecosystem, machine learning, and real-time analytics. Provides hybrid and multi-cloud deployment options.

Established enterprise data warehousing solution offering comprehensive capabilities for data warehousing, data lakes, and analytics. Known for scalability and hybrid cloud environment support. Provides advanced analytics, workload management, and integration with popular BI tools.

Cloud data platform supporting data engineering, collaborative data science, machine learning, and analytics. Built on Apache Spark with Delta Lake for reliable data lakes. Ideal for organizations focusing on advanced analytics, ML workflows, and collaborative data science with notebooks.

High-performance, self-managing data management service with automated patching, upgrading, and tuning. Particularly beneficial for enterprises in Oracle ecosystem or seeking highly automated data management. Features include automatic indexing, scaling, and security patching.

Cloud-native data platform supporting data warehousing, data lakes, data engineering, data science, and data sharing. Architecture separates compute and storage for independent scaling. Features include zero-copy cloning, time travel, automatic scaling, and multi-cloud support. Pay only for resources used.

Comprehensive enterprise data governance solution offering data cataloging, data lineage, policy management, privacy management, and compliance features. Provides AI-powered data intelligence platform for data discovery, quality, and governance. Integrates with modern data stacks and offers API access for Python integration.