A cloud data warehouse is a database that is stored in a managed cloud service and optimized for scalable analytics and BI.
For decades, data warehouses have been a staple of enterprise analytics and reporting. They were not designed to deal with today’s rapid data growth nor keep up with the ever-changing demands of end-users.
Cloud data warehousing allows you to grow and shrink your data warehouses quickly to meet changing business requirements and budgets. A cloud-based data warehouse can be thought of as a traditional data warehouse. It stores data from many different sources, including IoT, CRM, and finance systems.
A cloud-based data warehouse has highly structured and unified data that is ready to support many different business intelligence and analysis use cases.
The key features
- Massively Parallel Processing (MPP): Cloud-based data warehouses that provide big data support use MPP architectures to deliver high-performance queries on large volumes of data. MPP architectures consist of multiple servers that run simultaneously and distribute processing and input/output loads.
- Columnar data storage: MPP data warehouses, which are usually columnar stores, are the best for analytics. Columnar databases store and process data in columns, instead of rows, and allow for aggregate queries to run much faster. This is the type of database that is often used for reporting.
Cloud Data Warehouse Benefits
The cloud data warehouse is a key solution to modern business intelligence and analysis. It allows organizations to use advanced analytics to gain company insights that can help improve operations and customer service, and ultimately, gain a competitive advantage.
Modern cloud architectures offer the best of both data warehousing and flexibility of big data platforms while costing a fraction of traditional solutions. We will discuss the benefits of a cloud-based data warehouse in terms of performance, scalability, and cost.
More Powerful Computing: Cloud data warehouses provide faster computing and deliver real-time cloud analytics using data sourced from many data sources. This allows business users to get better insights faster.
Scalability Cloud-based storage offers instant and almost unlimited storage. It’s also easy to scale up as your storage requirements grow. Cloud storage is much cheaper than an on-premises warehouse and requires you to buy new hardware.
Overhead You will need to have a dedicated server room and skilled staff to manage, upgrade, and troubleshoot your data warehouse. Cloud data warehouses are much more cost-effective than traditional physical hardware and have no allocated office space.
Cloud Data Warehouse Automation
Modern data integration platforms automate data warehouse lifecycles to speed up the availability of analytics-ready data. A model-driven approach to data engineering will allow your data engineers to design, deploy, manage and catalog purpose-built cloud data warehouses more quickly than traditional methods.
Cloud Data Warehouse Vendors
Many cloud-based data warehouse platforms are available, such as Amazon Redshift or Google BigQuery. But there are also many important factors to consider when choosing the right solution for your organization.
While many cloud data platforms have similar capabilities, pricing, scalability, and architecture will vary. Security features, speed, and other factors can also vary between cloud data warehouse solutions.
We compare the top four vendors for your enterprise data warehouse.
Amazon vs. Azure vs. Google vs. Snowflake
Amazon Redshift: The first widely adopted cloud data warehouse.
Cloud-based data warehousing has been available only as an off-premise option for many years. Amazon Web Services (AWS), in November 2012, launched Redshift, a cloud-based, fully managed data warehouse service that can store petabytes of data. While it wasn’t the first cloud-based warehouse, it gained market share by being adopted. Redshift’s SQL dialect, which is based upon PostgreSQL and is well-understood by analysts around the world, uses an architecture that is familiar to many users of on-premises data warehouses.
Start with just a few gigabytes and grow to petabytes. This allows you to gain new insights from customer and business data.
Launching a cluster of Amazon Redshift nodes is the first step in creating a Redshift data warehouse. Once you have provisioned your cluster, you can upload your data set and perform data analysis queries. Amazon Redshift provides fast query performance regardless of how large your data set is. It uses familiar SQL-based tools as well as business intelligence applications to deliver high-quality results.
Microsoft Azure Synapse analytics: SQL beyond data warehouse.
Azure Synapse Analytics, a new analytics service, combines enterprise data warehousing with big-data analytics. You can query data with either serverless, on-demand or provisioned resources. Azure Synapse provides a single platform to access, prepare, manage and serve data for business intelligence (BI), and machine learning (ML).
Azure Synapse’s core is a distributed, cloud-native SQL processing engine. It is built on the SQL Server foundation to power your most complex enterprise data warehouse workloads. Azure SQL Data Warehouse (SQL DW), like other cloud MPP options, separates storage from computing and billing for each. Azure Synapse stores relational tables data in columnar storage. It abstracts physical machines and represents compute power as data warehouse units (DWUs) using Azure Synapse. This allows users to seamlessly and easily scale compute resources.
Synapse Analytics is a single user interface that unifies a variety of analytics workloads such as data warehouses and data lakes. An SQL Engine, Apache Spark, and Azure Data Lake Storage (ADLS) are combined to give users the ability to manage both data warehouses/data lakes as well as data preparation for ML tasks. Azure Synapse supports both horizontal and vertical scaling of the data warehouse. Vertically, by changing the service level or placing the database into an elastic pool. Horizontally, by adding data warehouse units.
Google BigQuery: A serverless solution.
BigQuery is serverless, fully managed data warehouse that scales automatically to meet storage and computing power requirements. Google doesn’t expect that you manage your data warehouse infrastructure. BigQuery hides many details about the underlying hardware, database, and nodes. Its elasticity works right out of the box. It’s easy to get started with Google Cloud Platform (GCP) by creating an account, loading a table, and running a query. Google will take care of everything else. It is the best cloud data warehouse solution.
BigQuery is a columnar and ANSI database that can process terabytes to petabytes of data at amazing speeds. BigQuery GIS also allows you to do spatial analysis with familiar SQL. With BigQuery ML, you can also quickly create and operate ML models from large-scale structured or semistructured data. BigQuery BI Engine also supports interactive dashboarding.
BigQuery architecture has many components. Borg is the compute. Colossus is distributed storage. Jupiter is the network. Dremel is Dremel’s execution engine.
Snowflake Cloud Data warehouse: The first multi-cloud data storage facility.
Snowflake, a fully managed MPP cloud-based MPP data warehouse, runs on AWS and GCP as well as Azure. Snowflake is unlike other data warehouses and does not run on its own cloud. Snowflake has a common code base that can be used to create new codes and move data to other clouds.
Snowflake users can create as many virtual warehouses to run parallel and isolated queries. Snowflake allows for high concurrency because it separates storage and computing so that multiple warehouses can simultaneously access the exact same data source.
Snowflake’s data warehouse can be accessed via a web browser, command line, analytics platform, or Snowflake’s ODBC, JDBC, or other supported drivers. The platform supports ACID-compliant relational processing. It also has native support for document storage formats like JSON, Avro, and Parquet.