We cover the pros and cons of each of these options and dive into the factors you’ll need to consider when choosing a cloud data warehouse.
Join the DZone community and get the full member experience. Data helps companies take the guesswork out of decision-making. Teams can use data-driven evidence to decide which products to build, which features to add, and which growth initiatives to pursue. And, such insights-driven businesses grow at an annual rate of over 30%. But, there’s a difference between being merely data-aware and insights-driven. Discovering insights requires finding a way to analyze data in near real-time, which is where cloud data warehouses play a vital role. As scalable repositories of data, warehouses allow businesses to find insights by storing and analyzing huge amounts of structured and semi-structured data. And, running a data warehouse is more than a technical initiative. It’s vital to the overall business strategy and can inform an array of future product, marketing, and engineering decisions. But, choosing a cloud data warehouse provider can be challenging. Users have to evaluate costs, performance, the ability to handle real-time workloads, and other parameters to decide which vendor best fits their needs. To help with these efforts, we analyze four cloud data warehouses: Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Snowflake. We cover the pros and cons of each of these options and dive into the factors you’ll need to consider when choosing a cloud data warehouse. A data warehouse is a system that brings data from various sources to a central repository and prepares it for quick retrieval. Data warehouses usually contain structured and semi-structured data pulled from transactional systems, operational databases, and other sources. Engineers and analysts use this data for business intelligence and various other purposes. Data warehouses can be implemented on-premise, in the cloud, or as a mix of both. The on-premise approach requires having physical servers, which makes scaling more expensive and challenging as users have to buy more hardware. Storing data online is less expensive, and scaling is nearly automated. A data warehouse can be used for various tasks. You can use it to store historical data in a unified environment that acts as a single source of truth. Users from an entire organization can then rely on that repository for day-to-day tasks. Data warehouses can also unify and then analyze data streams from the web, customer relationship management (CRM), mobile, and other apps. Today’s companies use an ever-growing number of software tools; pulling data from multiple sources, transforming it into consumable formats, and storing it in a warehouse is vital for making sense of data. And, with valuable data stored in warehouses, you can go beyond traditional analytics tools and query data with SQL to discover deep business insights. For instance, companies use Google Analytics (GA) to learn how customers engage with their apps or websites. But, the depth of insights users can discover is limited by the properties of GA. A better way would be to connect GA with a data warehouse that already stores data from platforms such as Salesforce, Zendesk, Stripe, and others. With all your data stored in one place, it’s much easier to analyze it, compare different variables, and produce insightful data visualizations. Conventional wisdom says you can probably use an OLTP database such as PostgreSQL unless you have terabytes or petabytes of complex data sets. However, cloud computing has made data warehousing cost-effective for even smaller data volumes. For instance, BigQuery is free for the first terabyte of query processing. Also, the total cost of ownership of serverless cloud data warehouses makes analytics simple. Not to mention, there is an expansive ecosystem for data integration, data observability, and business intelligence on top of popular cloud data warehousing tools that can accelerate your analytical operations. Many of today’s new cloud data warehouses are built using solutions from major vendors such as Amazon Redshift, Google BigQuery, Microsoft Azure Synapse Analytics, and Snowflake. Major vendors differ in costs or technical details, but they also share some common traits. Their cloud data warehouses are highly reliable. While outages or failures might happen, data replication and other reliability features ensure your data is backed up and can be quickly retrieved. Amazon, Google, Microsoft, and Snowflake also offer highly scalable cloud data warehouses. Their solutions use massively parallel processing (MPP), a storage structure that handles multiple operations simultaneously, to rapidly scale up or down storage and compute resources. And, data is stored in columnar format to achieve better compression and querying. Compared to on-premise data warehouses, cloud alternatives are more scalable, faster, go live in minutes, and are always up to date. Options for ingesting streaming data: Alternative methods: Alternative methods: Snowflake is a cloud data warehouse that runs on top of the Google Cloud, Microsoft Azure, and AWS cloud infrastructure. As the service doesn’t run on its own cloud but uses major public cloud vendors, it’s easier for it to move data across clouds and regions. Snowflake supports a nearly unlimited number of concurrent users and can be run with almost zero maintenance or administration.
