Data Warehouse reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.
Products classified in the overall Data Warehouse category are similar in many regards and help companies of all sizes solve their business problems. However, small business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Small Business Data Warehouse to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Small Business Data Warehouse category.
In addition to qualifying for inclusion in the Data Warehouse Software category, to qualify for inclusion in the Small Business Data Warehouse Software category, a product must have at least 10 reviews left by a reviewer from a small business.
Tens of thousands of customers use Amazon Redshift, a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Snowflake delivers the Data Cloud — a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflake’s platform is the engine that powers and provides access to the Data
BigQuery is Google's fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don't need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. BigQuery is a powerful Big Data analytics platform used by all types of organizations, from startups to Fortune 500 companies.
About IBM Db2 IBM believes in unlocking the potential of your data, not throttling it. We hold our databases to a higher standard, making it easy to deploy your data wherever it's needed, fluidly adapting to your changing needs and integrating with multiple platforms, languages and workloads. IBM Db2 is supported across Linux, Unix, and Windows operating systems.
Panoply is the world’s first Smart Cloud Data Warehouse. Panoply delivers the industry’s fastest time to insights by eliminating the development and coding typically associated with transforming, integrating, and managing data. Panoply’s proprietary AI technology automatically enriches, transforms and optimizes complex data, making it simple to gain actionable insights. The company, based in San Francisco and Tel Aviv, is privately held and funded by investors such as Intel Capital, 500 Startups
The Vertica Analytics Platform is built for the scale and complexity of today's data-driven world. We are trusted by thousands of leading, data-driven enterprises including Bank of America, Etsy, Twitter, Intuit, Uber and more to deliver speed, scale and reliability on mission-critical analytics, at a lower total cost of ownership than legacy systems. Vertica combines the power of a high-performance, massively parallel processing SQL query engine with advanced analytics and machine learning so
Advanced analytics meets traditional business intelligence with Pivotal Greenplum, the world’s first fully-featured, multi-cloud, massively parallel processing (MPP) data platform based on the open source Greenplum Database. Pivotal Greenplum provides comprehensive and integrated analytics on multi-structured data. Powered by one of the world’s most advanced cost-based query optimizers, Pivotal Greenplum delivers unmatched analytical query performance on massive volumes of data.
Data warehouse technology is used as a storage mechanism, different than traditional database technology. These tools are a key component of modern business intelligence operations, used as centralized repositories for data coming from multiple sources within a company. They can then be used in partnership with ETL tools to normalize and deliver information and data sets. Data warehouse solutions are designed with integration and analysis in mind. They are not designed like other databases to be queried in a variety of different ways. This helps users without knowledge of SQL or other common querying languages to extract information and data from storage.
Most data warehouse technology comes with features for data cleansing and normalization, so data can be stored in a variety of forms. This allows data from sales, marketing, research, and other departments to be stored in their natural forms but cleansed for comparative analysis.
Key Benefits of Data Warehouse Software
Data warehouses are a good option for companies with existing, cross-departmental data. These tools are better designed to handle analytics, significantly more than they are for data entry. They can help management and other employees who rely on large amounts of data and in-depth analysis during the decision-making process.
By making information available to users in any role, a data warehouse connected across departments can reduce siloing and poor communication frequently found in growing companies. It can also make it easier for users without technical backgrounds to perform self-service data requests.
Data warehouses can help users of all kinds improve the performance of data storage and usage through a simplified operational process. The tools will also help users create customized workflows that pull in data from multiple sources and present them to users in a digestible way. Overall, they can simplify processes for storage, retrieval, analysis, and visualization.
Data sources — Data warehouses typically rely on a range of data sources. The data can come from multiple sources, such as spreadsheets, banking systems, and software that ranges from SQL server and relational databases to legacy systems. These features help users consolidate data that they hope to use during the decision-making process.
Data marts — Data warehouses are organized into individual subsections. These segmented storage locations within the data warehouse are typically relevant to an individual team or department.
Scaling — Scaling allows the data warehouse to expand storage capacity and functionality while maintaining balanced workloads. This helps facilitate a growing demand for requests and expanding sets of information.
Autoscaling — While many tools allow administrators to control over scaling storage, autoscaling features help to reduce the manual aspects. This is done with automation tools or bots that scale services and data automatically or on demand.
Data sharing — Data sharing features offer collaborative functionality for sharing queries and data sets. These can be edited or maintained between users and potentially sent to customers or business partners.
Data discovery — Search tools provide the ability to search vast, global data sets to find relevant information. This allows users self-service access and navigation to multiple datasets.
Data modeling — Data modeling tools help users structure and edit data in a manner that enables quick and accurate insight extraction. They also help translate raw data into a more digestible format.
Compliance — Compliance features monitor assets and enforce security policies. Many can also audit assets to support compliance with PII, GDPR, HIPAA, PCI, and other regulatory standards.
Data lakes — A data lake is very similar to a data warehouse, but it typically stores a larger variety of data such as server logs, network activity, or any other non-traditional dataset or historical data that may not be imported into a data warehouse.
Real-time analytics — Real-time analytics features provide information in its most recent state and update users as soon as it changes. This will prevent the need to continually update data sets and simplifies the use of streaming data.
Data warehousing technology has a number of key components that create its overall architecture. A few of those are the database, data sources, data staging areas, presentation tools, and integration tools.
Database — The database storage warehouse itself consists of multiple operational data stores and data marts, where information is stored within the warehouse. Here, the data is somewhat organized; unstructured data remains behind and has not been normalized or cleaned.
Data sources — The data sources provide the database with its information. These sources can be virtually anything containing information, from spreadsheets to other SaaS tools used in the various departments of a company.
Data staging — Data staging areas are used to normalize and structure information. These transitional storage areas are often used during ETL processes where information is transformed, consolidated, aligned, and eventually exported.
Presentation tools — Once data has been cleansed and normalized within the staging area, data will be transferred to data marts for access from users. They may be exported at that point or paired with business intelligence tools for further visualization and analysis.
Integration tools — Integration tools are used both in the collection of information from its various data sources, as well as dispensing information after it has been normalized or modeled. These tools help facilitate the input of information and utilize the data being stored within a data warehouse.