Total Products under this Category: 120
Last updated: August 06, 2026
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Highlighted products: Databricks, Google Cloud BigQuery, Snowflake, SAP Datasphere, IBM watsonx.data, Amazon Redshift, Teradata Autonomous Knowledge Platform, and VMware Greenplum.
Underlying data: [Grid® JSON](https://www.g2.com/categories/data-warehouse/grids.json?focus%5B%5D=databricks&focus%5B%5D=google-cloud-bigquery&focus%5B%5D=snowflake&focus%5B%5D=sap-datasphere&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=amazon-redshift&focus%5B%5D=teradata-autonomous-knowledge-platform&focus%5B%5D=vmware-greenplum)
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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.
BigQuery is an AI-ready, petabyte-scale, and cost-effective data warehouse that lets you run analytics over vast amounts of data in near real time. Store 10 GiB of data and run up to 1 TiB of queries for free per month.
Average Rating: 4.5/5.0
Total Reviews: 1,145
AI-generated summary from verified user reviews
"Easy-to-Use Cloud Tool with Shareable, Saved Queries"
Rating: 4.0/5.0 stars
— Reetika P.
"Scalable, Secure BigQuery That Connects Seamlessly Across Services"
Rating: 5.0/5.0 stars
— Aayush M.
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value.
Average Rating: 4.6/5.0
Total Reviews: 1,334
AI-generated summary from verified user reviews
"Reliable Platform for Building Scalable Data Pipelines"
Rating: 5.0/5.0 stars
— aravind k.
"Databricks Streamlines ETL and Analytics with Scalable Notebooks"
Rating: 5.0/5.0 stars
— Diana C.
Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).
Average Rating: 4.6/5.0
Total Reviews: 714
AI-generated summary from verified user reviews
"Elastic Scaling and Fast Analytics with Snowflake"
Rating: 4.5/5.0 stars
— Ravindra N.
"Snowflake Simplifies Data Management at Scale"
Rating: 4.0/5.0 stars
— Harshil A.
SAP Datasphere is a unified service for data integration, cataloging, semantic modeling, data warehousing, and virtualizing workloads across all your data. It enables every data professional to deliver seamless and scalable access to mission-critical business data. SAP Datasphere, and its open data ecosystem, is the foundation for a business data fabric.
Average Rating: 4.2/5.0
Total Reviews: 165
AI-generated summary from verified user reviews
"SAP Datasphere A Powerful Platform for Data Integration and Real-Time Business Insights"
Rating: 5.0/5.0 stars
— Muzammil M.
"SAP Datasphere Simplifies Multi-Source Data Consolidation and Integration"
Rating: 4.5/5.0 stars
— Nijat I.
IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data_Trial
Average Rating: 4.4/5.0
Total Reviews: 167
AI-generated summary from verified user reviews
"Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve"
Rating: 4.0/5.0 stars
— Arkajit D.
"Clean, Smooth UI with Excellent Onboarding and Infrastructure Visuals"
Rating: 4.0/5.0 stars
— Aliasgar B.
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.
Average Rating: 4.3/5.0
Total Reviews: 371
AI-generated summary from verified user reviews
"Scalable and Efficient Cloud Data Platform"
Rating: 4.5/5.0 stars
— Swaroop W.
"Powerful Analytics Tool with Some Flexibility Limitations"
Rating: 4.0/5.0 stars
— Atharva P.
Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI. Learn more at Teradata.com.
Average Rating: 4.3/5.0
Total Reviews: 356
AI-generated summary from verified user reviews
"Teradata Vantage Fast Query Performance and Strong Analytics for Big Data"
Rating: 5.0/5.0 stars
— Muzammil M.
"Teradata Vantage Excels at Big Data Processing and Advanced Analytics"
Rating: 4.5/5.0 stars
— Nijat I.
Parallel Data Warehouse offers scalability to hundreds of terabytes and high performance through a massively parallel processing architecture.
Average Rating: 4.5/5.0
Total Reviews: 78
AI-generated summary from verified user reviews
Rating: 4.5/5.0 stars
— Kavya S.
"Improved performance and scalability: an excellent experience"
Rating: 5.0/5.0 stars
— Verified User in Computer & Network Security
Advanced analytics meets traditional business intelligence with VMware Greenplum, the world’s first fully-featured, multi-cloud, massively parallel processing (MPP) data platform based on the open source Greenplum Database. Greenplum provides comprehensive and integrated analytics on multi-structured data. Powered by one of the world’s most advanced cost-based query optimizers, VMware Greenplum delivers unmatched analytical query performance on massive volumes of data.
Average Rating: 4.3/5.0
Total Reviews: 55
"Open-Source MPP Database That Supercharges Large-Scale Analytics"
Rating: 5.0/5.0 stars
— deepeshkumar G.
"Greenplum - BIg Data Database"
Rating: 4.0/5.0 stars
— Naveen A.
Built to run the world’s mission-critical workloads. Designed by the world’s leading database experts, IBM Db2 empowers developers, enterprise architects, and data engineers to run low-latency transactions and real-time analytics equipped for the most demanding workloads. From microservices to AI workloads, Db2 is the tested, resilient, and hybrid database providing the extreme availability, built-in refined security, effortless scalability, and intelligent automation for systems that run the world.
Average Rating: 4.1/5.0
Total Reviews: 599
AI-generated summary from verified user reviews
"Comprehensive Review of DB2 on IBM i (AS/400)"
Rating: 4.5/5.0 stars
— Swapnil T.
"IBM Db2: A Reliable and High-Performance Enterprise Database Platform"
Rating: 4.5/5.0 stars
— Vinodh P.
Integrates database, server, storage and analytics into a single system with petabyte scalability. Fast analytics Provides a high-performance, massively parallel system that enables you to gain insight from your data and perform analytics on very large data volumes. Smart, efficient queries Simplifies analytics by consolidating all activity in one place, where the data resides. Simplified infrastructure Easy to deploy and manage; simplifies your data warehouse and analytic infrastructure. Does not require tuning, indexing or aggregated tables and needs minimal administration. Advanced security Enhanced data security is provided through self-encrypting drives as well as support for the Kerberos authentication protocol. Integrated platform Supports thousands of users, unifying data warehouse, Hadoop and business intelligence with advanced analytics.
Average Rating: 4.1/5.0
Total Reviews: 68
AI-generated summary from verified user reviews
"Efficiency Unleashed: A Comprehensive Review of IBM Netezza Performance Server"
Rating: 4.0/5.0 stars
— Verified User in Food Production
"Unleashing intelligence with IBM Netezza, driving data analysis and expediting Insights."
Rating: 5.0/5.0 stars
— Gunavardhan R.
Exasol is the world’s most powerful Analytics Engine, purpose-built to handle the most demanding data workloads at an unmatched price / performance ratio. In-memory architecture Want to process 3 billion rows in 3 seconds, not 3 hours? Exasol manages memory cache automatically, only bringing what's needed into the database for dramatically faster access times. Automatic query tuning Enjoy optimized performance while minimizing data administration overhead. Exasol uses intelligent, proprietary algorithms to self-tune queries on the fly -- adding and removing indices automatically – so you can bring true self-service BI to your organization. User defined functions (UDF) When you need more than a SQL statement, UDF scripts allow you to program your own analysis. Take your unique machine learning and data ingest scripts written in Python, R, and Lua, and run them in our database engine. Through UDF scripts, you'll get a highly flexible interface for nearly every requirement, allowing you to bring in data quickly from wherever it lives. In addition to being the fastest, Exasol also leads in the TPC price-performance metrics, meaning everyone in your organization can take advantage of unrivaled in-memory speed at a low price. And, unlike our competitors, Exasol allows you to choose the deployment destination. Deploy in the cloud, on-premises, or hybrid to meet your organization's unique needs and preferred vendors.
Average Rating: 4.7/5.0
Total Reviews: 23
AI-generated summary from verified user reviews
"Fast, Powerful Analytical Tool"
Rating: 4.5/5.0 stars
— Verified User in Computer & Network Security
"Light-Speed Performance and Superior Support"
Rating: 5.0/5.0 stars
— Björn B.
Dremio is the pioneer of The Agentic Lakehouse—the only data platform built for agents, managed by agents. Organizations need to transform ideas into actions at unprecedented speed—Dremio delivers this agility by equipping AI agents with federated data access, unstructured data processing, and rich business context through its AI Semantic Layer. In the agentic-era, data engineering teams can’t manually tune performance for thousands of users and agents asking unpredictable questions every second. Dremio’s Agentic Lakehouse autonomously manages itself, removing undifferentiated management tasks, allowing engineers to focus on initiatives that drive business results. Dremio’s agentic lakehouse automatically optimizes queries, reorganizes data, and maintains performance at any scale. Dremio is trusted by thousands of global enterprises including Shell, TD Bank, and Michelin, and built on open standards. Dremio co-created Apache Polaris and Apache Arrow, and it's the only lakehouse built natively on Apache Iceberg, Polaris, and Arrow.
Average Rating: 4.6/5.0
Total Reviews: 65
AI-generated summary from verified user reviews
"Flexible SQL for Handling Data from Many Sources"
Rating: 4.5/5.0 stars
— Tariel (Tato) B.
"Evaluating Dremio for Enterprise Data Analytics"
Rating: 4.5/5.0 stars
— Guilherme A.
Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineers, data engineers, data scientists, and analysts. It belongs to the Data Platform, Data Lakehouse, and Data Engineering software categories and supports flexible deployment across cloud, on-premise, and hybrid environments. Ilum enables technical teams to build, operate, and scale modern data infrastructure using open standards. It integrates tools for batch processing, stream processing, notebook-based exploration, workflow orchestration, and business intelligence, All In a Single Platform. Ilum supports modern open table formats like Delta Lake, Apache Iceberg, Apache Hudi, and Apache Paimon. It also offers native integration with Apache Spark and Trino for compute, with Apache Flink support currently in development. Key features include: - SQL Editor: Query Delta, Iceberg, Hudi, or Spark SQL with autocomplete, result previews, and metadata inspection. - Data Lineage & Catalog: Visualize data flow using OpenLineage and explore datasets through a searchable Data Catalog. - Notebook Integration: Use built-in Jupyter notebooks pre-wired to Spark, metadata, and your data environment for exploration or modeling. - Spark Job Management: Submit, monitor, and debug Spark jobs with integrated logs, metrics, scheduling, and a built-in Spark History Server. - Trino Support: Run federated queries across multiple data sources using Trino directly from within Ilum. - Declarative Pipelines: Define repeatable ETL and analytics pipelines, with dependency tracking and recovery logic. - Automatic ERD Diagrams: Instantly generate ER diagrams from schemas to aid in data understanding and onboarding. - ML Experimentation & Tracking: Includes MLflow for managing experiments, tracking parameters, metrics, and artifacts, fully integrated with notebooks and data pipelines to streamline model development workflows. - AI Integration & Deployment: Supports both classical ML and modern AI use cases, including GenAI workflows, vector search, and embedding-based applications. Models can be registered, versioned, and deployed for inference within declarative pipelines. - Built-in AI Agent Interface: Ilum integrates, providing a GPT-style interface to interact with your data, trigger pipelines, generate SQL, or explore metadata using natural language, bringing GenAI capabilities directly into your data platform. - BI Dashboards: Native support for Apache Superset, with JDBC integration for Tableau, Power BI, and other BI tools. Additional highlights: - Multi-Cluster Management: Connect multiple Spark or Kubernetes clusters to scale and isolate workloads. - Fine-Grained Access Control: LDAP, OAuth2, and Hydra integration for secure, role-based access. - Hybrid Ready: Designed to replace Databricks or Cloudera in environments where cloud adoption is partial, regulated, or not possible.
Average Rating: 4.9/5.0
Total Reviews: 23
AI-generated summary from verified user reviews
"From Hadoop to K8s with lower TCO"
Rating: 5.0/5.0 stars
— Mark D.
"Seamless Integration and Unified Features for Advanced Users"
Rating: 5.0/5.0 stars
— Jan L.
SAP Business Data Cloud is a fully managed software-as-a-service (SaaS) solution that unifies and governs SAP data and connects with third-party data. As an evolution of the company's data, planning, and analytics solutions, SAP Business Data Cloud brings together SAP Datasphere, SAP Analytics Cloud, and SAP Business Warehouse with a unified experience that delivers insights across all lines of business. In addition, SAP Databricks is natively available in Business Data Cloud - bringing the power of Databricks Data Intelligence Platform capabilities to the product. SAP Business Data Cloud connects data by leveraging business data fabric principles, making it easier to discover, share, govern, and model this data. It includes SAP Databricks as a first-party data service. The platform combines prebuilt applications and data products across all lines of business. It provides fully managed, curated data products across all lines of business and eliminate the costs of data extracts. Users can build on SAP’s curated data products with their domain expertise, and deliver Intelligent Applications through the Business Data Cloud ecosystem. These intelligent applications are adaptive, AI-powered applications that learn from your data, understand business context, and act on your behalf to transform business outcomes.
Average Rating: 4.2/5.0
Total Reviews: 75
AI-generated summary from verified user reviews
Rating: 4.0/5.0 stars
— Maria Francesca I.
"Centralized Data Management with User-Friendly UI"
Rating: 4.0/5.0 stars
— Tejas Kumar V.
Data warehouse technology is used as a storage mechanism that pulls data from multiple disparate data sources into one single data store in an organized and efficient way to enable analytics and reporting for better decision-making. It is different from traditional database technology which is only capable of recording data. Data warehouse solutions are designed with integration and analysis in mind; and not like other databases that are designed to be queried in a variety of ways. This helps users without knowledge of SQL or other common querying languages to extract information from storage.
A data warehouse acts as a single data repository that is an analytical and reporting database used to store historical data pulled from various disparate data sources. It also enables data retrieval through complex queries using online analytical processing (OLAP).
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.
Data warehouse solutions enable users to gain critical insights into their data through improved seamless self-service business intelligence (BI) capabilities. Though the purpose of the software remains the same, it differs in the mode of deployment and architecture. A data warehouse solution can be deployed both on the cloud and on-premises.
Cloud data warehouse
With cloud data warehouses, businesses can scale horizontally to hold increased storage and compute requirements. A data warehouse deployed on the cloud provides an improved infrastructure that lets companies focus more on delivering better and faster insights rather than managing a full house of servers on premises. These solutions provide cost control as organizations pay for what they use.
On-premises or license data warehouse
An on-premises data warehouse software lets organizations buy one time, deploy in-house, and enable control over their hardware and software infrastructure. This deployment solution requires a consultant to help with installation and ongoing support. One advantage of on-premises data warehouse solutions is that it gives complete control and access over the data within an organization, helping minimize security risks.
Data warehouses help organizations execute an effective data strategy, they feed structured and standardized data into BI tools which provide data professionals with high-level insights for decision-making. The following are some core features of data warehouse software:
Data source connections: Data warehouses typically rely on a range of data sources. The data can come from disparate sources, such as spreadsheets, banking systems, and software that ranges from SQL servers and relational databases to legacy systems. This feature helps users pull data that they hope to use during the decision-making process.
Data mart: Data warehouses are organized into individual subsections. These segmented storage locations within the data warehouse are typically relevant to an individual team or department. Data warehouse solutions enable users to create data marts within them.
Scaling: Scaling allows the data warehouse to expand storage capacity and functionality while maintaining balanced workloads. This helps facilitate the growing demand for requests and expanding sets of information.
Autoscaling: While many tools allow administrators to control 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. This also helps to audit assets to support compliance with personally identifiable information (PII), General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and other regulatory standards.
Data staging: Data staging areas are used to normalize and structure information. These transitional storage areas are often used during extract, transform, and load (ETL) processes where information is transformed, consolidated, aligned, and eventually exported.
Presentation tools: Once data has been cleansed and normalized within the staging area, it will be transferred to data marts for access from users. They may be exported at that point or paired with BI tools for further visualization and data 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.
Data transformation: This feature enables functions like data cleansing, data deduplication, data validation, summarization, and more. Data transformation is needed to convert the data into a format that can be used by BI tools to extract actionable insights in a seamless manner.
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.
Other features of data warehouse software: AI/ML Integration and Data Lake Integrations.
Data warehouses pull data from multiple disparate sources across departments within an organization. This data flows from various CRM systems, financial systems, ERP software, and more in real time. They act as decision support systems that are designed to store historical data, further processed and transformed to make it available for decision makers to gain meaningful and valuable insights. These solutions provide a single source of truth for all the data within an organization to make data-driven decisions.
Improved BI: Organizations majorly use data warehouses to support their analytics and BI requirements. Data warehouses facilitate centralized data storage in a quick and easy-to-access manner which further benefits BI implementations through effective analytics and better business decision making. Thus, these solutions help gain fast, accurate, and relevant insights into their data.
Increased return on investment (ROI): Organizations achieve an increase in revenue due to cost savings. Deploying data warehouse solutions helps organizations consolidate data from multiple disparate sources in a specific high-quality format at one single repository, making it easily available to access and analyze better. Data warehousing solutions also help improve operational efficiency and productivity.
Provides competitive advantage: Data within data warehouses is pulled from multiple disparate sources from within an organization and stored in a standardized format, ready to be analyzed. This allows quick and easy access to data and helps save a lot of time in deriving insights. They enable data professionals to identify and evaluate key threats and opportunities through effective business data analysis.
Improves operational workflow: Data in a data warehouse is often transformed and cleaned before being loaded into it. This ensures that the data being used is good in quality and the insights generated from the data can be trusted to be accurate. This can improve the operational efficiency of businesses.
Data warehousing solutions focus on data relevant to business analytics and organize and optimize it to enable efficient analysis. This software provides an easy interface for business analysts.
Data analysts and data scientists: These employees use data warehouses to get a centralized view of data across an organization to gain valuable insights in terms of being able to answer questions required for strategic decision making.
Related solutions that can be used together with data warehouses include:
Databases: Databases consist of a large family of tools used to store information digitally. There are a wide variety of databases such as relational databases software, object-oriented databases software, and graph databases. They can be used to store virtually any kind of data set, depending on their nature, but vary greatly between one another.
ETL tools: ETL is the most common way using which data is extracted from a data warehouse. These tools have long been used to facilitate the use of heterogeneous information sources and transform them into presentation-ready data formats.
Big data processing and distribution software: Big data processing and distribution software often work in tandem with data warehouses to process and distribute vast sums of information prior to storage. These tools help improve the warehouse’s scalability and processing power, which improves exploration compared to ETL tools.
Analytics platforms: To implement an effective and efficient analytics system, companies require well-structured and designed data warehouses. Data warehouses can be explained as solutions for data integration which further enable reporting and analytics. Data warehouses are an essential component of analytics systems; therefore a poorly-designed data warehouse can lead to lower value from the insights generated and further impact business decision-making measures. Analytics tools are associated with data warehousing in the form of reporting and analysis of information.
Software solutions can come with their own set of challenges.
On-premises data warehouse solutions: On-premises data warehouse solutions require managing and maintenance of hardware and software infrastructure and services in-house. Organizations require dedicated teams to implement these solutions. On-premises data warehouses cannot upscale on demand. Thus, scaling up to meet changing requirements will move organizations to replace systems.
Data quality: Data comes in data warehouses from multiple sources within organizations. Inconsistent data like duplicates, and missing information can lead to encountering errors. Poor or error-prone data quality can result in inaccurate reports and insights, which can lead to poor decision-making.
If a company is just starting out and looking to purchase the first data warehouse solution, or maybe an organization needs to update a legacy system--wherever a business is in its buying process, g2.com can help select the best data warehouse software for the business.
The particular business pain points might be related to unstructured and disparate data sources that must be analyzed well to use it for decision-making. If the company has amassed a lot of data, the need is to look for a solution that can help organize and structure that data to create a centralized view for analysis. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.
Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.
Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a data warehouse software.
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
Conduct demos
To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Choose a selection team
Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.
Negotiation
Just because something is written on a company’s pricing page, does not mean it is gospel (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.
Final decision
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
Data warehouse solutions are often sold as standalone products. They can be integrated with other BI and analytics tools. These typically come in two types of pricing models—flat rate and on demand.
How are Data Warehouse Solutions Implemented?
An organization could either decide to buy a commercial data warehouse or build an in-house data warehouse. Either way requires proper planning in terms of architecture and aligning the data warehouse project to the company goals because the end purpose is to obtain valuable insights for business leaders for strategic decision-making.
Data warehouse implementation can be done in the following ways: enterprise data warehouse, operational data store, and data mart.
Operational data store: An operational database (ODS) is designed to handle current operational data. The insights derived from this data primarily support the improvement of operational processes.
Enterprise data warehouse (EDW): This is a centralized data repository that collects enterprise data from multiple sources across the enterprise and makes it available for analysis to provide actionable insights.
Data mart: It can be considered as a subset of a data warehouse. It is focused on a specific division of business like sales, marketing, and finance. Data marts deliver data in small sets or partitions to provide easy and efficient access.
Who is Responsible for Data Warehouse Solution Implementation?
The deployment of a data warehouse requires the participation of multiple stakeholders. Some of them are as follows:
C-suite executives: These sets of people help users understand the long-term goals and strategies of an organization with regard to the data projects. They play a major role in scoping the data projects along with the project managers and the data team to help them understand what kind of data can be valuable to the organization for decision making.
Project managers: They are responsible for overseeing the overall project in terms of budget, schedules, deadlines, and project roadblocks. The project manager is assigned with the task to communicate the progress of the project to the senior management.
IT team: These teams consist of business analysts, technical architects, ETL experts, and specialists. This team plays a role in supporting the data projects helping execute activities like developing the data warehouse, connecting data sources, executing ETL processes, and more. They may be required to support the system if it’s an on-premises deployment.
What Does the Implementation Process Look Like for Data Warehouse Solutions?
The implementation process of a data warehouse solution can be broken down into the following steps:
Gathering and defining requirements: This step involves understanding the organization’s long-term business strategies and goals. It also covers various other criteria in terms of the kind of analysis and reporting required, as well as hardware, software, testing, implementation, and training of users. This step involves multiple stakeholders starting from the C-suite decisions, data, and analytics team, IT support, and the data governance team.
Data warehouse environment: As the next step, users must decide which deployment model is suitable: on-premises, public or private cloud, or hybrid cloud. Public cloud is considered one of the least expensive models as the cloud provider takes care of managing and maintenance of the infrastructure hardware requirements.
Data modeling: One of the crucial steps in data warehouse implementation is deciding on the data model. Every data source has a specific data scheme, picking up a single schema that is a fit for all is required.
Connecting data sources through ETL process: This step includes data extraction from multiple disparate sources, transforming it through converting the data from the source schema to the assigned destination schema and further loading it into the data warehouses. Transformation of the data also includes a couple of other actions that can be performed on the dataset like validation, enrichment, and other data health measures.
Integration to BI and analytics tools: Once a data warehouse system is set up, the next step involves integrating the BI tool being used by the organization with the warehouse data. This facilitates reporting and analytics which leads to delivering faster and easy insights for better decision making.
Testing and validating the system: This step includes the end-to-end testing of the entire data warehouse system. The system can be tested on various sets of parameters like data quality and integrity checks, the performance of the system, and analyzing whether it fulfills the end-user requirements in terms of reporting and analytics.
Shifting to cloud data warehousing solutions
Organizations are increasingly adopting cloud data warehouses to achieve improved scalability and performance. This shift helps them focus more on managing their business activities than managing a server block. Cloud data warehouse solutions also let organizations access easy real-time data from multiple sources, enabling them to gain better insights quickly. Companies can also achieve cost-effectiveness with data warehouses deployed on the cloud because it’s less expensive to scale a cloud data warehouse than one deployed on-premises. Also, buyers end up paying for the resources that they use, which further improves operational efficiency.
Moving towards DWaaS
Organizations are moving towards data warehouse as a service (DWaaS) as it lets buyers take advantage of eliminating hardware and software procurement, configuration, and maintenance work as a third party is responsible for these. Starting from data warehouse administration to setting up a data warehouse team, the providers are responsible for it.
