Cloud data warehouse solutions are the lifeline of any enterprise today. They help teams use advanced analytics to gain critical insights, which help businesses improve their operations, customer service, and overall processes.
Think of it like this: You probably have an insane amount of data, and some of it is useful but the rest is just operational noise. To put your data to best use, you need to draw insights and analytics. For that you need a powerful cloud data warehouse solution that lets you efficiently pool your data to help you make more informed decisions.
Nowadays, there are a lot of cloud data warehouse solutions that are easier to use, faster, and more scalable than in the past. With modern data architecture, you get the combined power of a data warehouse, the elasticity of the cloud, and the flexibility of big data platforms at a much more affordable cost than with legacy systems.
But with more and more options available on the market, it can be hard to know where to start. So we compiled a list of the best data warehouse platforms you can use today. We have given a comprehensive overview of every tool, including its use cases, top features, pros, cons, and pricing.
Let’s get started.
The 5 best cloud data warehouse solutions in 2023
Google BigQuery is a fully managed and serverless enterprise data warehouse available in the Google Cloud Platform (GCP). It has a built-in query engine that combines capabilities like business intelligence, geographical analysis, and machine learning to help you manage and analyze your data. Its serverless architecture lets end users run SQL queries on terabytes of data in a few seconds, giving you high performance with zero infrastructure management.
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or “heavy” queries that operate using a large set of data. This means it’s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great option for you.
- Using basic SQL, data analysts and data scientists can design and operationalize machine learning models directly within BigQuery ML (BQML).
- BigQuery comes with multi-cloud capabilities that allow you to analyze data across clouds using standard SQL.
- It synchronizes data across heterogeneous applications, databases, and cloud storage systems reliably with minimal latency using Datastream.
- It uses natural language processing (NLP) to make it easy for anyone to access the data insights they need.
- Features like BQML and federated queries help reduce the time spent moving data to the data warehouse in a traditional environment.
- BigQuery lets you create dashboards, connect new data sources, control data visibility permissions, automate notifications, and schedule data processing jobs quickly.
- Running complex analytical SQL-based queries under large sets of data feels like a breeze.
- It fits well with data visualization tools like Google Data Studio.
- It automatically provides replicated storage in multiple destinations and high availability without any additional charge or setup.
- At first, data management can be slightly overwhelming for non-technical users on BigQuery.
- The pricing can be steep depending on the size of the data you’re storing/fetching.
Free trial and paid pricing plans available
Snowflake is one of the most popular data warehousing solutions on the market and delivers an incredible experience across multiple public clouds. By using Snowflake, companies can pull data from various business intelligence tools to do reporting and analytics without any database administration, thus avoiding high overhead costs. Unlike other data warehousing services, Snowflake provides per-second pricing. And given its high-speed data processing capabilities, it truly makes for an affordable option for larger enterprises.
Snowflake is perfect for companies that are growing rapidly, and there are good reasons for that. You pay for only what you use. In addition, it offers amazingly fast data queries, reduces unnecessary complexity, and gives you self-service access to all the additional functionalities you may need.
- Snowflake can load and optimize data from a range of data sources, both structured and unstructured, including JSON, XML, and Avro.
- It provides cloud data security measures like always-on data encryption in transit and at rest.
- It offers plenty of useful features such as Time Travel historical data access, Fail-safe data recovery, zero-copy cloning, and more.
- Snowflake requires zero management and infrastructure.
- It offers high cloud storage capacity, making it a perfect option for businesses that have a large amount of data.
- It provides a seamless experience across multiple public clouds so you can execute different analytic workloads whenever required.
- It allows you to scale clusters up and down almost instantly, so you can do data analysis on scale.
- The cost is directly proportional to the amount of the system’s load.
- Managing access to effectively track usage and cost across the organization can be a challenge.
- You need a data engineering team to manage Snowflake data operations, so it’s not the ideal option for non-technical users.
- It doesn’t currently support unstructured data.
Free trial and paid pricing plans available
Amazon Redshift is a fully managed cloud data warehouse that uses SQL to analyze structured and semi-structured data across operational databases, data warehouses, and data lakes. It leverages AWS-designed hardware and machine learning to deliver a great user experience and affordable pricing at any scale. Plus it has massive parallel processing that enables fast performance.
If you need to execute complex business intelligence (BI) and analytics workloads that require excellent performance at any scale, Amazon Redshift is a superb cloud provider.
- Redshift is powered by built-in AI capabilities to process and break larger queries into smaller, more efficient parts.
- It has massive parallel processing that distributes SQL operations and makes data processing faster.
- It provides functions like analytics, math, date, and time, which users can employ for data analytics.
- Redshift’s workload management (WLM) configuration makes it easier for developers to set rules and priorities around queries and processes without going into cluster management details.
- It’s easy to set up, delivers real-time insights, and offers high performance.
- Redshift’s automatic hourly snapshot feature is quite helpful for easily restoring data.
- It collaborates with AWS S3 and allows you to query against the exabyte of data stored in S3.
- Redshift offers great scalability, allowing users to easily scale out within minutes to match their requirements.
- At a beginner level, it has a steep learning curve.
- The service doesn’t scale infinitely. You will have to manage load capacity.
- It only runs on AWS, which makes it slightly expensive when compared to other data warehouse providers.
Free trial with on-demand paid plans available
Firebolt is a next-gen cloud data warehousing platform that makes data “fly” at an enormous scale. It offers a higher speed of processing while still being affordable. It’s known to be taking on some of the most popular tools in this list including Google’s BigQuery and Snowflake, as it can process petabyte-scale data in just a few seconds. Firebolt is the only data warehouse with decoupled compute and storage that supports both semi-structured and ad-hoc data analytics with high performance at scale.
Firebolt comes with all you need to analyze and handle large volumes of data at a great speed. Its overall features make it the best choice for big tech companies, business intelligence enterprises, and any customer-facing organization that needs to parse a lot of data and get real-time insights.
- Firebolt uses native lambda expressions to handle semi-structured data and comes with the best storage for SQL.
- It separates data storage from compute, which allows engineers to execute compute-heavy workloads such as ETL or ELT jobs.
- It supports multi-master continuous ingestion, single-row inserts, and automatic rebalances.
- It uses optimized aggregate, sparse data, and join indexes for improved query performance.
- It’s built for the needs of data engineers to deliver outstanding query speeds in a serverless and fully elastic manner.
- It makes it easy to index and query semi-structured data and import database snapshots.
- It’s highly customizable and provides granular control of all your resources—choose an engine type, select the different engine specifications, and more.
- It offers a flexible pay-as-you-go pricing model.
- Instances need to be spun up manually each time you want to run queries.
- It comes with initial administration and other overhead costs.
- The platform is newer, so it while it comes with speed, it also comes with quirks.
Databricks is an open, multi-cloud platform that combines the best of data lakes and data warehouses into a unified architecture. It provides unified data analytics platforms for your team including data analysts, data engineers, data scientists, and business analysts.
Databricks is a one-stop destination for your data requirements. For instance, it can derive insights using Spark SQL, build predictive models using Spark ML, and build connections to visualization tools such as Power BI, Tableau, and QlikView.
Databricks helps eliminate data silos and fragmented systems in an organization, which makes it an ideal fit for enterprises that have a large volume of disparate data.
- It supports multiple coding languages in the same environment. So data engineers could use Scala for model predictions, Spark SQL for data transformation tasks, Python for model performance evaluation, and more, all in a unified platform.
- It provides highly scalable Spark jobs for data science—users can process both small- and large-scale jobs easily.
- It can connect with multiple data sources including on-premises SQL services, JSON, and CSV.
- Databricks makes it easy to set up, test, and deploy new pipelines.
- It has great flexibility across different ecosystems including AWS, Microsoft Azure, and GCP.
- It offers the possibility of combining different programming languages including Python, SQL, and R.
- It supports frameworks, libraries, scripting languages, IDEs, and tools.
- Databricks is built on open-source technologies which means better support from the community for documentation, skills in the talent pool, tutorials, and more.
- It supports SQL endpoints that help users connect to almost anything stored in AWS S3 in a secure manner.
- It’s not built for non-technical users and needs you to be a proficient programmer to be able to fully use its functionalities.
- The solution was initially built around a notebook model and you’ll notice that in the way that all functionality is structured.
Free trial with paid pricing
Which cloud data warehouse solution is best for you?
Most teams are tasked with “doing more with less”, and selecting a powerful cloud data warehouse platform can be a great start.
Here are our top recommendations:
- If you want both your data team to collaborate seamlessly using a modern, easy, and fast cloud data warehousing solution, Snowflake is the best option for you.
- If you’re looking for a solution that integrates seamlessly with your existing cloud platform, choose Google BigQuery or Amazon Redshift.
Choosing the right data warehousing solution is critical for any data-driven enterprise. The next step is choosing an orchestration platform that allows you to automate and action on that data. Whether you want an alerts on your customer data or need to deploy ETL/ELT pipelines, Shipyard has you covered. With integrations to every tool in your data stack (including those listed in this article), it’s easy to build solutions with your data in a matter of minutes.Get started today with Shipyard’s free Developer Plan!
Conclusion: So above is the Top 5 Cloud Data Warehouses in 2023 article. Hopefully with this article you can help you in life, always follow and read our good articles on the website: W Tài Liệu