Introduction

A data warehouse is a store of data from which data can be pulled out for analytics , for business insights and other functions such as machine learning. Typically, data warehouses a fed with data from data sources such as transaction files, log files, web, mobile, databases, amongst others.

Data warehouses are important in that they allow businesses to store, manage and query data required to gain insights from analytics. Some tools that enable this include SQL, python, R, Power BI and Tableau.
We will be reviewing three data warehouses currently available.

Overview:

Amazon Redshift

Amazon Redshift is a cloud-based data warehouse provided by Amazon web services.
Some features of Amazon Redshift to lookout for include:

Scalability: Amazon Redshift allows for easy scaling of storage and computing resources as needed, making it suitable for handling large amounts of data.

Cost–effectiveness: Amazon Redshift is designed to be cost-effective, with a pricing model that gives you the ability to scale resources up as needed. You can start with a plan of $0.25 without commitment and move up to $1000 per terabyte per year. According to Amazon, they are the only ones who offer on-demand pricing without upfront costs.

Performance: Amazon Redshift uses columnar storage and advanced compression algorithms to deliver fast query performance. It also utilizes parallel processing and optimized data distribution to improve performance.
Supports third party querying tools: It allows for the use of querying tools such as SQL clients tools or data science tools like Power BI, tableau, as well as its in- house Amazon QuickSight.

In-house services integration: Amazon Redshift can integrate with other AWS services, such as AWS Lake formation, AWS Glue, Amazon EMR and Amazon QuickSight, to handle a majority of the data pipeline configuration. Utilizing these together helps to handle the reporting process without as much hassle.

Security: Amazon Redshift provides a number of security features, including end-to-end encryption, Network isolation which allows you to create your own firewall, and dynamic data masking – that enables customers to hide certain data from users.

A lot of companies make use of Amazon redshift for their data warehousing. Some notable ones include:

Nasdaq: Amazon Redshift’s scalability, performance and flexibility enabled Nasdaq’s operations. Nasdaq migrated from it’s on premises data warehouse to Amazon redshift, to increase scale and performance, and reduce operational costs. This solution, by 2018, enabled Nasdaq to ingest financial markets data from thousands of sources, about 30billion to 55 billion records at more than 4 terabytes.
General Electric: The company relies on Amazon Redshift for gaining insights from the data which it collects from its wind turbines around the world. The insights enables GE to perform predictive preventive maintenance of its turbines.

Magellan RX: Amazon redshift helped them to reduce ETL time by 70%, from 11 to 3 hours – reduced operational costs, and analytics query time. The solution had helped them to on-board customer data faster.

Snowflake

Snowflake is another cloud-based data warehousing platform.
Some of its features are:
Multiple data types support: Snowflake allows for structured, semi-structured and unstructured data types.

Security: it features TLS communication for all communication between clients and server. It also allows for column-level security, enabling masking policies for tables or views, and row access policies for tables and views.
Multiple-view management: capability od managing tge warehouse virtually, using GUI and command line.
Platform support through Snowflake extension: Snowflake allows for the connectivity to visual audio code by means of Snowflake extension.

Data import & export: it supports the bulk loading and unloading of data from most flat & delimited data files, compressed files, loading files(in JSON, Avro, OPC, Praquet & XML format). Additionally, it allows for the continuous loading of data from files.
Data sharing: It allows data to be shared with other accounts, as well as consumption of data from other accounts.
Database replication and failover: It provides support for the replication and syncing of databases across different Snowflake accounts. Additionally, it allows for database failover configuration to other Snowflake accounts, making sure business continues and for disaster recovery.

Organizing data: In Snowflake, data can be organized in schemas, databases and tables. There’s also an unlimited number of databases, schemas abs tables that can ve created.
The following companies have made use of Snowflake:

Autodesk: At some point, loading a BI dashboard was a challenge as this occurred half of the time, arising from problems with their Spark and Athena ecosystem. Another was the burden incurred to operate it, and or wasn’t cost effective when thinking of scaling.
Using Snowflake, which has a multi-cluster shared architecture and elastic scalability feature, enabled Autodesk to solve its performance issues, leading to an improved experience for BI users. More so, the almost zero maintenance offered by Snowflake allowed Autodesk’s technical staff more time to focus on increasing insights.
Bumble: After its IPO in 2021, Bumble saw increased number of users and new data – which ultimately lead to increased demand for internal reporting for product analytics, business and developer metrics. Both data engineers, analysts and software engineers were in need of restoring from their on-premises system which couldn’t conveniently handle everyone’s request at the same time and their Head of Data knew it.
Bumble’s choice to go for Snowflake after comparison with AWS and Google BigQuery helped solved their reporting and query problems previously encountered – leading to uncompromised operations.

Google BigQuery
It is an enterprise data warehouse with machine learning and BI capabilities, enabling customers to analyse data to make data driven decisions.

Some key features of BigQuery include:

Public datasets: Google enables free storage of data sets and allows its customers to query up to 1TB of data per month free of charge.

Petabyte Scale: BigQuery allows for storage up from petabytes to exabytes, enabling seamless scalability.
Real-time change, data capture and replication: Customers can use Datastream to manipulate data across heterogeneous databases at low latency.

Supports unity, management and governing of all data types: surveyed, semi-structured and unstructured data can ve queries using BigQuery.

Real-time Analytics with built-in query acceleration: Customers can analyze data almost immediately after streaming data is ingested.

Built-in Machine Learning: BigQuery has an in-house system which allows data scientists and analysts to build machine learning models of some different types.

Some companies that have used BigQuery include:
Proctor & Gamble: P&G have customer information. BigQuery assisted them in understanding their customers, as well as creating omni-channel customer journeys. This way meant they could serve the right audiences with the right content at the right time. P&G employed Google BigQuery for their data science needs and to serve larger audiences, due to its compatibility with offset visualization tools. The result helped P&G to hit business and analytics goals.
Home Depot: Home Depot was able to cut query time down to seconds and minutes, which previously took hours and days. Google BigQuery, at the time, helped Home Depot by providing timely data to help stock it’s over 50,000+ items at over 2,000 locations, ensuring website availability and providing relevant information through the call center.

Summary

In summary, all three data warehouses offer some similar features, with some notable differences.Overall, your choice of data warehouse will have to be made based on your budget, uses case and perhaps complexity.