Which data warehouses offer the best performance for storing and querying big data?

In terms of Performance, A Data Warehouse that would be highly recommended is surely AWS Redshift & then Google BigQuery. AWS Redshift offers immense durability & scalability when it comes to heavy loads & fast querying performance with a smooth experience. Google BigQuery is also one of the most preferred choices, surely because of its renowned capabilities & commutation power with an exceptional integration with Google AI services.

In my experience, below points need to be considered Ability to scale resources dynamically to match workload demands. Utilization of powerful processing units for executing complex queries quickly. Efficient storage mechanisms like columnar storage and compression techniques. Distributing query workloads across multiple nodes or clusters for faster data retrieval. Advanced techniques streamline query execution and minimize processing overhead. Efficient handling of multiple simultaneous queries without performance degradation. Distributing data across nodes to reduce data movement and network latency.

Use Azure Databricks if your focus is on big data processing, real-time analytics, and machine learning. Use Azure Synapse Analytics for a unified analytics experience that combines data warehousing and big data capabilities. Use Azure SQL Data Warehouse if you are looking for a traditional SQL-based data warehousing solution within the Azure ecosystem. Use Snowflake for a highly scalable, cloud-native data warehousing solution that works across multiple cloud platforms and supports various data formats.

One thing I have found helpful when evaluating data warehouse for performance is to thoroughly benchmark them using realistic workloads and data volumes.This helps identify potential bottlenecks and performance limitations early on,allowing for informed decision and optimization.

Properly indexed tables and optimized data layouts can significantly reduce query execution times by facilitating faster data retrieval and minimizing disk I/O operations.

There is no silver bullet to answer your question. As the answer depends on multiple aspects. - how fast is your data growing? - how much is it contributing to solving your business problems. - How much you are ready to pay for? - Managed data warehouse solutions are better if you have sufficient financing and business case that will give you best business continuity.

One example I have seen involves a company that initially opted for a data warehouse with limited storage capacity. As their data volume grew, they faced challenges with performance and scalability.Transitioning to a data warehouse solution offering elastic storage allowed them to accommodate their expanding data needs without compromising personal downtime.This shift not only resolved their immediate issue but also ensured they could scale efficiently as their data continue grow.

Look for a data warehouse solution that follows a pay-as-you-go pricing model for storage. This allows you to pay only for the storage capacity you need, without incurring upfront costs or over-provisioning resources.

Selecting a data warehouse with robust elastic storage capabilities ensures that your organization can efficiently manage growing data volumes while maintaining high performance and minimizing disruptions.

Evaluate the ease of integrating data from diverse sources such as databases, streaming platforms, IoT devices, and external APIs to ensure timely and comprehensive data acquisition.

By choosing a data warehouse with robust data processing capabilities, organizations can ensure efficient ETL processes, enabling faster and more accurate data analysis and insights.

Consider the use of query compilation and code generation techniques to transform high-level SQL queries into optimized executable code for faster execution.

By leveraging advanced query optimization techniques, a data warehouse can significantly enhance query performance, providing faster and more efficient access to big data for analysis and decision-making.

Implement automatic scaling policies that adjust resource allocation based on predefined thresholds or performance metrics. Plan for future growth and scalability requirements when designing and deploying your data warehouse infrastructure.

By selecting a data warehouse that excels in scalability and elasticity, organizations can ensure their data infrastructure remains robust and responsive to changing demands, providing consistent performance and cost efficiency.

Data Loading Speed: How fast can the data warehouse ingest new data? This is important for keeping your data fresh and insights up-to-date. Data Compression: Efficient data compression techniques can minimize storage requirements and improve query speeds by reducing the amount of data that needs to be processed. Hardware and Infrastructure: The underlying hardware and infrastructure of the data warehouse can significantly impact performance. Consider factors like CPU power, memory, and storage capacity.