What is a data warehouse software?

What is a data warehouse software? : In order to facilitate and support business intelligence (BI) activities , particularly analytics, a data warehouse is a particular type of data management system . Data warehouses frequently include significant amounts of historical data and are used only to conduct queries and analysis.

Read Detail Answer On What is a data warehouse software?

Organizations are turning to cloud-based technologies in today’s quickly evolving corporate environment for practical data collection, reporting, and analysis. Data warehousing, a key element of business intelligence that enables businesses to improve their performance, steps in at this point. It’s critical to comprehend data warehouses, how they work, and why they’re changing in today’s market.

In this article, we’ll provide an overview of Data Warehouse – explore key concepts like data warehouse architecture, characteristics of data warehouse, what is data management, the benefits of data warehouse, and data warehouse applications.  

What Is a Data Warehouse

Data warehouses serve as a central repository for storing and analyzing information to make better informed decisions An organization’s datawarehouse receives data from a variety of sources, typically on a regular basis, including transactional systems, relational databases, and other sources

A data warehouse is a centralized storage system that allows for the storing, analyzing, and interpreting of data in order to facilitate better decision-making Transactional systems, relational databases, and other sources providedata into data warehouses on a regular basis

An example of a data management system is a data warehouse, which supports and facilitates business intelligence (BI) activities, particularly analysis. Data warehouses typically include substantial amounts of historical data and are primarily created to simplify searches and analyses.

A data warehouse can be defined as a collection of organizational data and information extracted from operational sources and external data sources The data is periodically pulled from various internal applications like sales, marketing, and finance; customer-interface applications; as well as external partner systems This data is then made available for decision-makers to accessand analyze

So what is data warehouse? For a start, it is a comprehensive repository of current and historical information that is designed to enhance an organization’s performance. 

Key Characteristics of Data Warehouse

The main characteristics of a data warehouse are as follows:

  • Subject-Oriented

A data warehouse is subject-oriented since it provides topic-wise informationrather than the overall processes of a business. Such subjects may be sales, promotion, inventory, etc. For example, if you want to analyze your company’s sales data, you need to build a data warehouse that concentrates on sales. Such a warehouse would provide valuable information like ‘who was your best customer last year?’ or ‘who is likely to be your best customer in the coming year?’

  • Integrated

A data warehouse is developed by integrating datafrom varied sources into a consistent format. The data must be stored in the warehouse in a consistent and universally acceptable manner in terms of naming, format, and coding. This facilitates effective data analysis. 

  • Non-Volatile

Data once entered into a data warehouse must remain unchanged. All data isread-only. Previous data is not erased when current data is entered. This helps you to analyze what has happened and when. 

  • Time-Variant

The data stored in a data warehouse is documented with an element of time, either explicitly or implicitly. An example of time variance in Data Warehouse is exhibited in the Primary Key, which must have an element of time like the day, week, or month.

Database vs. DataWarehouse

Although a data warehouse and a conventional database are similar in some ways, they are not necessarily the same thing. The main distinction is that information is gathered for various transactional purposes in databases. To perform analytics, data is gathered in a data warehouse on a large scale. Real-time data is provided by databases, while large analytical queries can access data stored in warehouses.

Usually,data warehouse architecture comprises a three-tier structure.

Bottom Tier

The bottom tier or data warehouse server usually represents a relational database system. Back-end tools are used to cleanse, transform and feed data into this layer. 

Middle Tier

The middle tier represents an OLAP server that can be implemented in two ways

The ROLAP model, also known as Relational OLAP, is an extension of the relational database management system that converts multidimensional data processing into a relational process.

The MOLAP or multidimensional OLAP directly acts on multidimensional data and operations.

Top Tier

This is the front-end client interface that gets data out from the data warehouse. It holds various tools like query tools, analysis tools, reporting tools, and data mining tools. 

How Data Warehouse Works

Data Warehousing integrates data and information collected from various sources into one comprehensive database For example, a data warehouse might combine customer information from an organizations point-of-sale systems, its mailing lists, website, and comment cards It might also incorporate confidential information about employees, salary information, etc Businesses use such components of data warehouse to analyzecustomers

Data mining is one of the features of a data warehouse that involves looking for meaningful data patterns in vast volumes of data and devising innovative strategies for increased sales and profits.  

Types of Data Warehouse

There are three main types of data warehouse.

Enterprise Data Warehouse (EDW)

This type of warehouse serves as a key or central database that facilitates decision-support servicesthroughout the enterprise. The advantage to this type of warehouse is that it provides access to cross-organizational information, offers a unified approach to data representation, and allows running complex queries. 

Operational Data Store (ODS)

This type of data warehouse refreshes in real-time. It is often preferred for routine activities like storing employee records. It is required when data warehouse systems do not support reporting needs of the business. 

DataMart

A data mart is a subset of a data warehouse built to maintain a particular department, region, or business unit. Every department of a business has a central repository or data mart to store data. The data from the data mart is stored in the ODS periodically. The ODS then sends the data to the EDW, where it is stored and used.   

Data Warehouse Example

Let’s examine some instances of businesses utilizing data warehouses as a crucial component of daily operations.

Investment and Insurance companies use data warehouses to primarily analyze customer and market trends and allied data patterns In sub-sectors like Forex and stock markets, data warehouse plays a significant role because a single point difference can result in huge losses across the board

Retail chains use data warehouses for marketing and distribution so they can track products, look at pricing practices, and examine consumer buying trends. For the purposes of forecasting and business intelligence, they use data warehouse models.

Healthcare companies, on the other hand, use data warehouse concepts to generate treatment reports, share data with insurance companies and in research and medical units Healthcare systems depend heavily upon enterprise data warehouses becausethey need the latest, updated treatment information to save lives

Wondering what Data warehouse tools is? Well, these are software components used to perform several operations on an extensive data set These tools help to collect, read, write and transfer data from various sources What do data warehouses support? They are designed to support operations like data sorting, filtering, merging, etc

Data warehouse applications can be categorized as:

  • Query and reporting tools
  • Application Development tools
  • Data mining tools
  • OLAP tools

Some popular data warehouse tools are Xplenty, Amazon Redshift, Teradata, Oracle 12c, Informatica, IBM Infosphere, Cloudera, and Panoply. 

Benefits of Data Warehouse

Wondering why businesses need data warehousing? Well, there are severalbenefits of data warehouse for end users.

  • Improved data consistency
  • Better business decisions
  • Easier access to enterprise data for end-users
  • Better documentation of data
  • Reduced computer costs and higher productivity
  • Enabling end-users to ask ad-hoc queries or reports without deterring the performance of operational systems
  • Collection of related data from various sources into a place

Companies having dedicated Data Warehouse teams emerge ahead of others in key areas of product development, pricing, marketing, production time, historical analysis, forecasting, and customer satisfaction. Though data warehouses can be slightly expensive, they pay in the longrun. 

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What is the best data warehouse software? : The top ten data warehouse applications Amazon Redshift. Snowflake. Vertica. IBM Db2. BigQuery. Platform Lakehouse by Databricks. Microsoft. Performance Server for Netezza by IBM.
Is SQL a data warehouse tool? : SQL Server Integration Services is a Data warehousing tool that used to perform ETL operations; i e extract, transform and load data SQL Server Integration also includes a rich set of built-in tasks
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A Data Warehouse is a collection of software tools that help analyze large volumes of disparate data from varied sources to provide meaningful business insights. A Data warehouse is typically used to collect and analyze business data from heterogeneous sources.

Easily replicate all of your Cloud/SaaS data to any database or data warehouse in minutes CData Sync is aneasy-to-use data pipeline that helps you consolidate data from any application or data source into your Database or Data Warehouse of choice Connect the data that powers your business with BI, Analytics, and Machine Learning

Features:

  • From: More than 100+ enterprise data sources including popular CRM, ERP, Marketing Automation, Accounting, Collaboration, and more.
  • Automatedintelligent incremental data replication
  • Runs anywhere – On-premise or in the Cloud
  • Supports cloud data warehouses include Amazon Redshift, Snowflake, Salesforce and Big Query
  • It provides customer support via Chat, Email and Phone
  • CData Sync Supports compliance standards such as GDPR, PCI DSS, ISO 3166-1 and ISO 27001:2013
  • This tool also provide Real-time Change Data Capture, Advanced ETL and ELT transformations, Incremental loading, Scheduling andmonitoring and APIs and scripting
  • It supports 250+ data sources
  • Integrate with SQL Server, MySQL and Oracle
  • Supports output format such as PDF, DOC, RTF, ODT, CSV and HTML
  • Supported Platforms: Cloud, Windows, Linux and Mac
  • Price: Plans start at $3999 a Year
  • Free Trial: 30 Days Free Trial (No Credit Card Required)

👍 Pros👎 Cons

You can easily configure your connections with the program Advanced features aren’t documented
It is easy to replicate data into any warehouse in just a few minutes. Upgrade notifications are lacking.
Data can be replicated from more than 150 enterprise data sources  
A standard enterprise-class security features  

30-Day Free Trial

2) Integrate.io

Integrate. io is a data warehouse integration platform created for e-commerce. Integrate. With the aid of io, e-commerce companies can create a 360-degree view of their customers, create a single source of truth for data-driven decisions, enhance customer insights through improved operational insights, and boost ROI.

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Features:

  • Powerful, low-code data transformation offering.
  • Pull in data from any source that has a RestAPI. If no RestAPI exists, then you can create yourown with Integrate.io’s API Generator.
  • Send data to databases, on-prem, data warehouses, NetSuite, and Salesforce.
  • Integrate.io connects to all major E-commerce providers such as Shopify, NetSuite, BigCommerce, and Magento.
  • Meet all compliance requirements with security features like: field-level data encryption, SOC II certification, GDPR compliance, and data masking.
  • Supports cloud data warehouses include Amazon S3, Google Cloud Storage, HDFS, Microsoft AzureBlob Storage, MongoDB Atlas, etc.
  • It provides customer support via Live Chat, Email and Phone
  • Integrate.io Supports compliance standards such as SOC 2, ISO 27001, SOC 1 and SOC 2/SSAE 16/ISAE 3402 (Previously SAS 70 Type II)
  • This tool also provide Build a Customer 360 view, Easy Data Transformations, Simple Workflow Creation for Defining Dependencies Between Tasks, REST API, Salesforce to Salesforce Integrations, Data Security and Compliance, Diverse Data Source andDestination Options and Excellent Customer Support
  • It supports 100+ data sources
  • Integrate with SOC 2, ISO 27001, SOC 1 and SOC 2/SSAE 16/ISAE 3402 (Previously SAS 70 Type II)
  • Supports output format such as PDF, HTML, images and CSV
  • Supported Platforms: Cloud, Web-Based, Windows, and Linux
  • Price: Request a Quote from Sales
  • Free Trial: 14 Days Free Trial (No Credit Card Required)

👍 Pros👎 Cons

Responsive and quick customer service Error logs aren’t always helpful
It is easier to use and more flexible.  
Implementation with low-code  

Visit Integrate.io >>

14-Day Free Trial

3) QuerySurge

The RTTS company created the QuerySurge ETL testing solution. It was created specifically to automate testing of data warehouses and big data. It makes sure that the data that is extracted from data sources is preserved in the target systems as well.

Features:

  • Improve data quality & data governance
  • Accelerate your data delivery cycles
  • Helps to automate manual testing effort
  • Provide testing across the different platform likeOracle, Teradata, IBM, Amazon, Cloudera, etc.
  • It speeds up testing process up to 1,000 x and also providing up to 100% data coverage
  • It integrates an out-of-the-box DevOps solution for most Build, ETL & QA management software
  • Deliver shareable, automated email reports and data health dashboards
  • Supports cloud data warehouses include Amazon S3, Google Drive, Microsoft OneDrive and Dropbox
  • It provides customer supportvia Chat, Contact Form and Email
  • This tool also provide Files & APIs, Big Data & NoSQL, Collaboration, CRM & ERP, Accounting, Marketing and eCommerce
  • It supports 50+ data sources
  • Integrate with MySQL, Nonstop SQL, Oracle and PostgreSQL
  • Supports output format such as Excel, CSV and XML
  • Supported Platforms: Windows and Linux
  • Price: Plans start at $492 a Year
  • Free Trial: 30 DaysFree Trial

👍 Pros👎 Cons

The software integrates with a wide range of leading test management solutions. A number of features are locked behind premium subscriptions.
It provides a significant return on investment (ROI). A large dataset may take time to process, causing delays in automated pipelines.
You can test on more than 200 different platforms  
Speed up the data quality process  

Visit QuerySurge >>

30-Day Free Trial

4) Astera DW Builder

Astera DW Builder is an end-to-end data warehousing tool that enables business users to design, develop, and deploy high-volume data warehouses using a metadata-driven approach The solution offers a comprehensive data model designer and robust ETL/ELT capabilities that simplify deployment of a data warehouse on-premises or in the cloud

Features:

  • Built-in connectors for several on-premises industry leading databases, including SQL Server, Oracle, SAP HANA, etc., and cloud services such as Azure Cloud andAmazon.
  • Design your warehouse schema using different data modelling patterns, such as star schema, 3NF, and data vault.
  • Test and troubleshoot your data warehouse prior to deployment through a data model verification module.
  • Publish your data model through OData protocol to enable real-time querying.
  • Automate ETL operations through job scheduling and workflow automation.
  • Supports cloud data warehouses include Amazon Redshift, Microsoft Azure SynapseAnalytics, Snowflake, and others.
  • It provides customer support via Phone, Chat, Contact Form and Email
  • Astera DW Builder Supports compliance standards such as CCPA, GDPR, HIPAA, EDI 179 and ISO-8601
  • This tool also provide Accessibility, Scalability, Elasticity, More Data Storage, Improved Disaster Recovery, Data Cleansing, Data Transformation and Loading Business Intelligence and Data Analysis
  • It supports 40+ data sources
  • Integrate with SQL Server, Access,MySQL and PostgreSQL
  • Supports output format such as Excel and CSV
  • Supported Platforms: Windows, Mac and Linux
  • Price: Request a Quote from Sales
  • Free Trial: 14 Days Free Trial

👍 Pros👎 Cons

An easy-to-use tool for data transformation without coding A workflow can take hours to process a large dataset
The connectors are easily dragged and dropped Changes in data types and ranges are difficult to accommodate
Easy creation of reports  
An enhanced access to information and data.  
Provide intelligent data mapping services  

Visit DWBuilder >>

14-Day Free Trial

5) BiG EVAL

By continuously validating and observing the quality of the information, BiGEVAL maximizes the value of enterprise data. In the course of development, testing tasks are also automated. The distinct automation strategy and the straightforward user interface ensure same-day benefits.

Features:

  • Autopilot data quality measuring and testing, driven by meta data.
  • Fully customizable algorithms, rules and test behavior.
  • Gallery with hundreds of bestpractices validation templates ready to be used by you.
  • Deep insight analysis with clear dashboards and alerting processes.
  • Integration with hundreds of tools (e.g. Jira, ServiceNow, Slack, Teams …).
  • Embeddable into DataOps processes and DevOps CI/CD flows.
  • Hundreds of connectors to any kind of data (RDBMS, APIs, Flatfiles, Business applications, SaaS …).
  • Supports cloud data warehouses include Dynamics 365, Azure Data Lake, REST API and Google CloudPlatform
  • It provides customer support via Contact Form and Chat
  • BiG EVAL Supports compliance standards such as GDPR
  • This tool also provide Testcase Organization, Scripting, Analysis, Extensions, Alerts, Security, Migrations, Code Versioning and Audit Trail
  • It supports 10+ data sources
  • Integrate with MySQL, Oracle, PostgresSQL, SQL Server, Azure SQL Database, HBase and mongoDB
  • Supports output format such as Excel, JSON, PDF, XLSX and CSV
  • SupportedPlatforms: Web-Based
  • Price: Plans start at $99 a month. 8% Discount on Yearly Payment.
  • Free Trial: 14 Days Free Trial

👍 Pros👎 Cons

In-memory scripting and rules engine with high performance. There are limited options in the free version
A powerful tool that can be used to test and manage the quality of the data. Lack of customer support
The tool can be embedded into ticket systems, DevOps CD/CI flows, etc.  
This will help to maximize the coverage of the tests.  
Automate metadata-based testing from a data schema or metadata repository  

Visit BiG EVAL >>

14-Day Free Trial

6) Oracle Autonomous Database

Oracle data warehouse software is a collection of data which is treated as a unit The purpose ofthis database is to store and retrieve related information It helps the server to reliably manage huge amounts of data so that multiple users can access the same data

Features:

  • Distributes data in the same way across disks to offer uniform performance
  • Works for single-instance andreal application clusters
  • Offers real application testing
  • Common architecture between any Private Cloud and Oracle’s public cloud
  • Hi-Speed Connection to move large data
  • Works seamlessly with UNIX/Linux and Windows platforms
  • It provides support for virtualization
  • Allows connecting to the remote database, table, or view
  • Supports cloud data warehouses include Amazon S3, Microsoft Azure, etc.
  • It provides customer support via Chat andPhone
  • Oracle Autonomous Database Supports compliance standards such as ISO 8601, ISO/IEC 9075-1, ISO-3166, SOC 1, SOC 2 and GDPR
  • This tool also provide Auto-scaling, Auto-securing, Auto-tuning, Auto-backups, Auto-repairing, Auto-patching, Autonomous warehouse management, self-service data tools and analytics, Comprehensive data and privacy protection
  • It supports 20+ data sources
  • Integrate with MySQL and Oracle
  • Supports output format such as XML, JSON, CSV,HTML, PDF, TXT and DOC
  • Supported Platforms: Cloud-based
  • Price: Request a Quote from Sales
  • Free Trial: 30 Days Free Trial

👍 Pros👎 Cons

Simple and easy-to-use Initial setup of the system was quite complex
A good customer support system Monitoring via Oracle Enterprise Manager is not available
Automate data protection and security  
Faster, simpler, and more efficient transactions  

Download Link: https://www.oracle.com/autonomous-database/autonomous-data-warehouse/

7) Amazon RedShift

An easy-to-use, straightforward, and reasonably priced data warehouse tool is Amazon Redshift. Almost any type of data can be examined using standard SQL.

Features:

  • No Up-Front Costs for its installation
  • It allowsautomating most of the common administrative tasks to monitor, manage, and scale your data warehouse
  • Possible to change the number or type of nodes
  • Helps to enhance the reliability of the data warehouse cluster
  • Every data center is fully equipped with climate control
  • Continuously monitors the health of the cluster. It automatically re-replicates data from failed drives and replaces nodes when needed
  • Supports cloud data warehouses include Amazon S3
  • Itprovides customer support via Contact Form and Chat
  • Amazon RedShift Supports compliance standards such as PCI-DSS, HIPAA/HITECH, FedRAMP, GDPR, FIPS 140-2, and NIST 800-171
  • This tool also provide Easy analytics, Analyze all your data, Performance at any scale, Most secure and compliant
  • It supports 10+ data sources
  • Integrate with PostgreSQL, SQL Server, and MySQL
  • Supports output format such as TXT, PDF, XML, CSV , TSV , CLF , ELF , and JSON
  • Supportscloud data warehouses include Amazon S3
  • Supported Platforms: Cloud-based
  • Price: Request a Quote from Sales
  • Free Trial: 60 Days Free Trial

👍 Pros👎 Cons

It is fast and widely adopted.  
An easy-to-use administration system. This is not a multi-cloud solution.
It is capable of handling large databases with its ability to scale Requires a good understanding of the Sort and Dist keys
It has a massive storage capacity There is limited support for parallel uploads
It offers a consistent backup for your data  
A transparent and competitive pricing structure  

Download Link: https://aws.amazon.com/redshift/

8) Domo

Domo is a cloud-based Data warehouse management tool that easily integrates various types of data sources, including spreadsheets, databases, social media and almost allcloud-based or on-premise Data warehouse solutions

Features:

  • Help you to build your dream dashboard
  • Stay connected anywhere you go
  • Integrates allexisting business data
  • Helps you to get true insights into your business data
  • Connects all of your existing business data
  • Easy Communication & messaging platform
  • It provides support for ad-hoc queries using SQL
  • It can handle most concurrent users for running complex and multiple queries
  • Supports cloud data warehouses include SAP, snowflake, Google Analytics, Amazon S3, hadoop, Oracle, salesforce and MySQL
  • It provides customer supportvia Chat, Contact Form, Email and Phone
  • Domo Supports compliance standards such as GDPR, HIPAA, SOC 1/2 and ISOThis tool also provide Data Sharing & Embedded Analytics, Self-service Analytics, Data Sharing & Embedded Analytics
  • Integrate, Visualize, Data Apps, Cloud, Security and Governance
  • It supports 1000+ data sources
  • Integrate with MySQL and MongoDB
  • Supports output format such as ODT, CSV, XLS, XML and JSON
  • SupportedPlatforms: Windows, Mac and Linux
  • Price: Request a Quote from Sales
  • Free Trial: 30 Days Free Trial

👍 Pros👎 Cons

A powerful tool for the ETL and visualization of data. DOMO is very costly compared to other tools
It is easy to access The data from Domo is hard to extract
This is a cloud-native platform  
Connect Domo to any data source, physical or virtual  
Indicators of trends and problems  

Download Link: https://www.domo.com/product

9) SAP

SAP is a platform for integrated data management that maps every business process within an organization. It is an open client/server application suite for enterprises. It is one of the best data warehouse tools and has raised the bar for the best business information management solutions.

Features:

  • It provides highly flexible and most transparent business solutions
  • Theapplication developed using SAP can integrate with any system
  • It follows modular concept for the easy setup and space utilization
  • You can create a Database system that combines analytics and transactions. These next next-generation databases can be deployed on any device
  • Provide support for On-premise or cloud deployment
  • Simplified data warehouse architecture
  • Integration with SAP and non-SAP applications
  • Supports cloud data warehouses includeGoogle Cloud Storage, Azure Data Lake (ADL), Local File System [File), Google Cloud Storage (GCS), Hadoop File System (HDFS), Amazon S3, Microsoft Azure Blob Storage (WASB) and WebHDFS
  • It provides customer support via Chat, Contact Form and Phone
  • SAP Supports compliance standards such as ISO/IEC 27001, SOC, ISO 9001, ISO 22301, ISO/IEC 27018 and ISO/IEC 27017
  • This tool also provide Business Semantic Service, Secure Workspaces, Reuse of Existing Investments, Third-PartyContent, Customer Relationship Management, Project Management, Procurement, Supply Chain Management, Industry-Specific Functionality and Localization
  • Integrate with MySQL and MongoDB
  • Supports output format such as PDF, XSF, XML, HTML, PCL and TXT
  • Supported Platforms: Windows, Mac and Linux
  • Price: Plans start at $19 a month.
  • Free Trial: 14 Days Free Trial
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👍 Pros👎 Cons

SAP DWC could be a cost-effective option SAP Data Warehouse Cloud does not support application development
There is rich connectivity support for most SAP sources This feature does not support queries.
Designed to work best with SAP applications  
A fully featured cloud-based data warehouse  

Download Link: https://www.sap.com/india/products/data-warehouse-cloud.html

10) Informatica

Informatica Corporation created the data integration tool known as Informatica PowerCenter. The tool provides the ability to connect to and retrieve data from various sources.

Features:

  • It has a centralized error logging system which facilitates logging errors and rejecting data into relational tables
  • Build in Intelligence to improve performance
  • Limit the Session Log
  • Ability to Scale up Data Integration
  • Foundation for Data Architecture Modernization
  • Better designs with enforced best practices on code development
  • Code integration with external Software Configuration tools
  • Synchronization amongst geographicallydistributed team members
  • Supports cloud data warehouses include Amazon Redshift Workbook, Google Drive and Dropbox
  • It provides customer support via Chat, Contact Form and Phone
  • Informatica Supports compliance standards such as AICPA SOC 1, AICPA SOC 2, GDPR, ISO 8859-1, ISO 639 and ISO/IEC 19770-2
  • This tool also provide Optimization Engine, Task flow orchestration, Multi-cloud support, Codeless advanced integration, Intelligent structure discovery, API Creationand Management, Cloud B2B Gateway, Cloud Data Warehouse, Cloud Data Quality, Cloud Mass Ingestion, Cloud Integration Hub, Business Process Automation, Real-Time Data Integration, Application Integration and Hyperautomation and Consumption-based pricing
  • It supports 100+ data sources
  • Integrate with Microsoft SQL Server, Oracle, IBM DB2, PostgreSQL and ODBC
  • Supports output format such as PDF, HTML, Microsoft Excel, Text, RTF and XML
  • SupportedPlatforms: Microsoft Windows, Linux, Debian, and Mac OS
  • Price: Request a Quote from Sales.
  • Free Trial: 30 Days Free Trial

👍 Pros👎 Cons

Faster and more cost-effective There is a lack of sorting functionality in the Workflow Monitor
Data Integration with the Cloud The deployment process is a bit complicated.
The ability to access a wide range of data sources Lack of a possibility to do loops within informatica workflows.
Load stabilization and parallel processing  
Integration with standard APIs and tools that are easy to use  
The quality of technical support provided by the company  

Download link: https://www.informatica.com/products/cloud-data-integration.html

11) Talend Open Studio

A data warehousing tool created by Talend is called Open Studio, which is open source and free. It is made to combine, convert, and update data from various locations. An intuitive set of tools is provided by this tool, which greatly simplifies handling data. Additionally, big data integration, data quality, and master data management are all possible.

Features:

  • It supports extensive data integration transformations and complex process workflows
  • It is one of the best open source data warehousingtools that offer seamless connectivity for more than 900 different databases, files, and applications
  • This data warehouse open source tool can manage the design, creation, testing, deployment, etc of integration processes
  • Synchronize metadata across database platforms
  • Managing and monitoring tools to deploy and supervise the jobs
  • Supports cloud data warehouses include Google Cloud Storage
  • It provides customer support via Contact Form andChat
  • Talend Open Studio Supports compliance standards such as PCI DSS, GDPR, ISO/IEC 27001 and ISO-8859-1
  • This tool also provide Resolve issues before they occur, Take control of your supply chain and Build better business analytics
  • It supports 140+ data sources
  • Integrate with MS-SQL, Oracle, PostgreSQL, Sybase and SQLite
  • Supports output format such as PDF, HTML and CSV
  • Supports cloud data warehouses include Google Cloud Storage
  • SupportedPlatforms: Windows, Mac and Linux
  • Price: Request a Quote from Sales.
  • Free Trial: 14 Days Free Trial

👍 Pros👎 Cons

An easy-to-use drag-and-drop interface for creating complex applications Integration with some data sources can be challenging
It is easy to connect to databases on different platforms. Small-scale deployments in SMB environments are less suitable
It can be used for both qualitative and quantitative metrics.  
There are advanced scheduling and monitoring features available in the tool.  
Integration with standard APIs and tools that are easy to use  
The quality of technical support provided by the company  

Download Link: https://www.talend.com/products/talend-open-studio/

12) The Ab Initio software

The Ab Initio is a data warehousing tool that supports data analysis, batch processing, and GUI-based parallel processing. Data extraction, transformation, and loading are frequently done with it.

Features:

  • Meta data management
  • Business and Process Metadatamanagement
  • Ability to run, debug Ab Initio jobs and trace execution logs
  • Manage and run graphs and control the ETL processes
  • Components can execute simultaneously on various branches of a graph
  • Supports cloud data warehouses include Snowflake, Redshift, Synapse, RDS Aurora, BigQuery, AWS, Google Cloud, Microsoft Azure and Oracle Cloud
  • It provides customer support via Email and Phone
  • The Ab Initio software Supports compliance standards such as HIPAAand GDPR
  • This tool also provide Data Processing Platform, Cloud Native, Real-Time Digital Enablement, Futureproofing & Legacy Modernization, Searching, Scoring & Matching, Rules-Based Matching, and more.
  • It supports large number of data sources
  • Integrate with XML, JSON, protobuf, COBOL, ASN.1, EDIFACT, SWIFT, ISO20022, ICD10, and HL7
  • Supports output format such as XML, JSON and Excel
  • Supported Platforms: Windows and Linux
  • Price:Request a Quote from Sales

👍 Pros👎 Cons

ETL tool that can be used to process big data in a fast and effective way It is an expensive tool
Error handling takes much less time There are no training materials provided by the company.
It is easy to maintain There is no native scheduler built into the application
Ease of Debugging  
It has a user-friendly interface  

Download Link: https://www.abinitio.com/en/

13) TabLeau

Tableau Server is an online Data warehousing with 3 versions Desktop, Server, and Online. It is secure, shareable and mobile friendly ETL data warehousetechnology solution.

Features:

  • It is one of the best open source data warehouse tools that connects to any data source securely on-premise or in the cloud
  • Ideal tool for flexible deployment
  • Big data, live or in-memory
  • Designed for mobile-first approach
  • Securely Sharing and collaborating Data
  • Centrally manage metadata and security rules
  • Powerful management and monitoring
  • Get maximum value from your data with this business analytics platform
  • Share and collaborate in the cloud
  • Tableau seamlessly integrates with existing security protocols
  • Supports cloud data warehousesinclude Google Drive and Dropbox
  • It provides customer support via Email
  • TabLeau Supports compliance standards such as ISO 527, ISO-27001 and GDPR
  • This tool also provide Data Stories, Autosave in the browser, In-product Exchange, Advance Management for Tableau Cloud
  • It supports numerous data sources
  • Integrate with MySQL, MongoDB, Oracle and PostgreSQL
  • Supports output format such as XML, Excel and PDF
  • Supported Platforms:Windows and Mac
  • Price: Request a Quote from Sales
  • Free Trial: Life Time Free Basic Plan

👍 Pros👎 Cons

Very fast and easy to create visualizations Relatively high cost
Good customer support No change management or versioning
Data Interpreter Story-telling ability Importing custom visualization is a bit difficult.
Tableau offers a feature of visualization  
It helps you to handle a large amount of data  

Download Link: https://public.tableau.com/en-us/s/download

14) Pentaho

Pentaho is a Data Warehousing and BusinessAnalytics Platform It is one of the best data warehouse technologies that has a simplified and interactive approach which empowers business users to access, discover and merge all types and sizes of data

Features:

  • Enterprise platform to accelerate the data pipeline
  • Community Dashboard Editor allows the fast and efficient development and deployment
  • Big data integration without a need for coding
  • Simplified embedded analytics
  • Visualize data with custom dashboards
  • Operational reporting for mongo dB
  • Platform to accelerate the data pipeline
  • Supports cloud data warehouses include Google Drive and Dropbox
  • It provides customer support via Contact Form and Phone
  • Pentaho Supports compliance standardssuch as PCI DSS and GDPR
  • This tool also provide Storage Virtualization Operating System RF, In-System Replication software, Remote Replication software, High availability with global-active device, Data Mobility software, Data-at-rest encryption, CLI and API integration and Storage management software
  • It supports 40+ data sources
  • Integrate with SQL Server, MySQL, Oracle and PostgreSQL
  • Supports output format such as PDF, HTML, Excel, CSV, RTF and XML
  • SupportedPlatforms: Windows and Linux
  • Price: Request a Quote from Sales
  • Free Trial: 30 Days Free Trial

👍 Pros👎 Cons

Provides an easy-to-use interface Much slower tool evolution compared to other BI tools.
The capability of running on the Hadoop cluster Pentaho Business analytics offers a limited number of components.
Live technical support is available 24×7  
Flexible and native integration support for big data  

Download now: https://www.hitachivantara.com/en-us/solutions/modernize-digital-core/data-modernization/data-lakes-data-warehouses.html

15) BigQuery

Googles BigQuery is an enterprise-level data warehousing tool It is one of the best DWH tools that reduces the time for storing and querying massive datasets by enabling super-fast SQL queries It also controls access to both the project and also offering the feature of view or query the data

Features:

  • Offers flexible Data Ingestion
  • Read and write data in via Cloud Dataflow, Hadoop, and Spark.
  • Automatic Data Transfer Service
  • Full control over access to the data stored
  • Easy to read and writedata in BigQuery via Cloud Dataflow, Spark, and Hadoop
  • BigQuery provides cost control mechanisms
  • Supports cloud data warehouses include Netezza, Oracle, Redshift, Teradata, Snowflake to, Spark, TensorFlow, Dataflow, Apache Beam, MapReduce, Pandas, and scikit-learn
  • It provides customer support via Chat, Phone and Contact Form
  • BigQuery Supports compliance standards such as SOC 2, ‎ISO/IEC 27001, PCI DSS, ‎Hipaa and ‎FedRAMP
  • This tool also provide ML andpredictive modeling with BigQuery ML, Multicloud data analysis with BigQuery Omni, Interactive data analysis with BigQuery BI Engine, Geospatial analysis with BigQuery GIS, Serverless, and more.
  • It supports 5 data sources
  • Integrate with MySQL, PostgreSQL, and SQL Server
  • Supports output format such as CSV, JSON, HTML, PDF, GIF, TIFF, JPEG, PNG and BMP
  • Supported Platforms: Android, iOS, Mac, Linux and Windows
  • Price: Requesta Quote from Sales
  • Free Trial: Life Time Free Basic Plan

👍 Pros👎 Cons

For long-running queries, BigQuery performs much better It can be confusing to use several SQL dialects
The automated backup and restore of data The lack of support for updates and deletions
Almost all data sources are natively integrated. Limitations regarding the exporting of data
There are no limits to the size of the storage or the processing power  
It is very affordable to use BigQuery  
BigQuery supports low latency streaming  

Download now: https://cloud.google.com/bigquery/

FAQ

❓ What is a Data Warehouse?

A Data Warehousing is a central repository of the data integrated from various sources. Data Warehouse is considered as a core component forbusiness intelligence, which stores current and historical data into one place for creating analytical reports. The goal is to derive profitable insights from collected data.

💻 What are the Best Data Warehouse Tools?

⚡ What is Data Warehousing Tools?

Data Warehousing Tools are the software components used to perform various operations on a large volume of data. Data Warehousing tools are used to collect, read, write, and migrate large data from different sources. Datawarehouse tools also perform various operations on databases, data stores, and data warehouses like sorting, filtering, merging, aggregation, etc.

✅ Which factors should you consider while selecting a Data Warehouse Software?

We should consider the following factors while selecting a Data Warehouse Software:

  • Functionalities offered
  • Performance and Speed
  • Scalability and Usability features
  • Security and Reliability
  • Integration options
  • Data Types supported
  • Backup and Recovery support for data
  • Whether the software is Cloud-based or On-premise
Is a data warehouse a system or software? : In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence DWs are central repositories of integrated data from one or more disparate sources
Read Detail Answer On Is a data warehouse a system or software?

Data warehouse software is essential for companies looking to streamline their data centers or implement business intelligence software. Business intelligence software stores and analyzes huge volumes of data, and your company will need data warehouse software to keep each database organized and ensure the data stays consistent. Data warehousing software stores data from a variety of sources and makes it searchable, so you can easily find the information you need for analysis. To helpyou find the right data warehouse for your business, use this buyer’s guide covering the top data warehouse software and tools.

READ More:  What is the strategic approach?

Data warehouse software overview

  • What is Data Warehouse Software?
  • Key features of DataWarehouse Tools
  • Top Data Warehouse Solutions
    • Oracle Database
    • Grow
    • ClicData
    • Snowflake
    • Teradata Database
    • Panoply
    • Funnel
    • Google BigQuery

What is Data Warehouse Software?

In order for businesses to easily analyze the data, data warehouse software extracts, transforms, and loads (ETL) data from various sources and puts it in a common format. For a clearer view of the data, the software will also remove redundant information. Utilizing data warehouse software is primarily done by businesses to support analytics and facilitate access to summary data.

Data warehouse software pulls data from many different platforms and converts it into the same type. This allows it to be easily compared and analyzed from a single console.The system also eliminates redundancies in the information and cleans it in the event of incorrect or incomplete data. 

Business intelligence integration

While business intelligence (BI) and data warehouse software are usually kept as two separate technologies, they are closely related and help organizations improve their decision making. Data warehouse software storesdata in its raw form, while BI software displays the data in an easy-to-understand format and provides insights based on the information. Therefore, data warehouse software should integrate with your BI system to make retrieving and analyzing data easier. 

Relational databases

Relational databases store pieces of related data and control access to them The data is formatted into tables where organizations can easily see how the information is related One example of data thatmight be stored in a relational database is customer information When a customer places an order, the order is labeled with the customer ID and the relational database matches that ID with a billing and shipping address to quickly process the order and provide timely delivery

Also read: Top 10 Benefits of a Data Warehouse

Top DataWarehouse Solutions

Here are some of the top data warehouse solutions based on customer reviews and the features they provide.

Oracle Database

Key takeaway: Oracle Database is best for enterprise companies looking to leverage machine learning to improve their business insights. 

Oracle Database offers data warehousing and analytics to help companies better analyze theirdata and reach deeper insights. The platform includes machine learning (ML) capabilities, allowing developers to easily integrate ML into their Python, Ruby, or SQL applications. Oracle Database features a single management dashboard that improves performance and data access. It offers encryption, privileged access controls, activity monitoring, and more to meet compliance requirements and keep data secure.

Pros

  • Easy to move data
  • Comprehensivedocumentation and support
  • Tables make the database easily searchable

Cons

  • Can’t be deployed on-premises with Mac devices
  • Requires heavy resource usage, which slows down other programs

Grow

Key takeaway: Grow is best for small and medium-sized businesses that have a dedicated data analyst and need their data warehouse andbusiness intelligence platform combined into a single tool.

Grow makes it easier to pull and transform data from multiple sources and blend it to create dashboards that provide better company insights. It also includes no-code options to help citizen developers create custom calculations and summaries of relevant data. There are native integrations available for many of the most popular tools, including CRMs, social media platforms, accounting software, and marketing analytics. Growconnects data and creates helpful models, so organizations can store and analyze data within the same application.

Pros

  • Can pull data from a wide variety of platforms
  • Combine warehousing, ETL, and BI into one platform
  • Accommodates users of varying technical expertise

Cons

  • Fewer BI capabilities than pure BI platforms
  • Can occasionally have long load times

ClicData

Key takeaway: ClicData is best for agencies who want the ability to provide branded reports to their clients and use automation to reduce the amount of time they spend on data collection.

ClicData pulls information from over 150 different data sources, providing a simplified view of a company’s data. It uses a real-time API, meaning that it only pulls themost up-to-date data. The platform also keeps snapshots of historical data, allowing businesses to compare current and past trends or restore it if something goes wrong. ClicData allows users to create custom calculations and groups in order to get the most from their data. It can also be white-labeled, allowing agencies to share branded reports with their clients.

Pros

  • Custom dashboards provide clear results
  • Intuitive user interface
  • Easyto share dashboards

Cons

  • Steep learning curve
  • Some users want more templates

Snowflake

Key takeaway: Snowflake is best for companies that want to easily move their data to the cloud while still having the ability to access and analyze that data.

Snowflake offers a cloud-based data warehouse that provides asingle source for all data and is easy to scale as your business grows. It integrates easily with the major public cloud providers, like Amazon and Azure and requires very little maintenance. Snowflake provides companies greater access to their data with smart searches and high storage capacities, enabling them to make smarter decisions about their growth. The platform also automatically encrypts data both at rest and in transit to keep it secure while enabling you to share it with authorizedparties.

Pros

  • Easy to understand and learn
  • Allows analysts to use SQL to query data
  • Good documentation and community support

Cons

  • Only cloud-based which may not meet some compliance requirements
  • Can be expensive if not optimized properly

Teradata Database

Key takeaway: Teradata Database isbest for enterprise companies that need the ability to make smarter, time-based queries and organize data by location.

Teredata Database is a scalable, self-managing data warehouse that uses parallel operations to increase efficiency and performance. It supports small datasets all the way up to companies with thousands of users and many petabytes (PB) of data. With geospatial capabilities, the data warehouse can organize data based on global proximity. It automatically tracks the historyof an organization’s data and allows users to add time constraints to queries, like “how many sales did we make last month”. 

Pros

  • Can work with large amounts of data at once
  • Intuitive user interface
  • Offers custom integration solutions

Cons

  • More expensive than similar products
  • Requires training to be successful

Panoply

Key takeaway: Panoply is best for companies that have data analytics and engineers without much coding experience and need their data warehouse to scale with them.

Panoply is a low-code data warehouse platform that includes unlimited integrations and warehouse management. The system automatically updates to pull the most up-to-date data and provides built-in performance monitoring. It’s easy to scale as an organization grows and needsmore storage, and pricing is based on the amount of data a company imports, rather than the number of users. There are also pre-built SQL queries to make analyzing data easier. Panoply automatically detects the type of data that a business adds, reducing the amount of busywork.

Pros

  • Helpful and responsive support
  • Data pipelines are quick and easy to set up
  • Easy to add and connect data sources

Cons

  • Nobuilt-in visualization tools
  • Large files sometimes have long load times

Google BigQuery

Key takeaway: Funnel is best for marketing teams and agencies that want a better, more unified view of their ad spend and how their campaigns are performing.

Funnel is a data warehouse solution geared towards marketing and advertisinganalytics. It collects data from a large number of advertising sources and allows marketing teams to get a better idea of where their ad spend is going. Businesses can group ad accounts to accurately reflect how their business operates and analyze campaign performance across multiple channels. Funnel also provides insightful reports, allowing marketing teams to form stronger insights and make better decisions about their advertising campaigns.

Cons

  • Limited dashboardingfeatures
  • Only works with marketing and advertising data

Google BigQuery

Key takeaway: Google BigQuery is best for companies using Google’s Cloud Platform that want the ability to incorporate AI and ML into their decision-making process.

A serverless data warehouse solution called Google BigQuery aids in the increased agility of businesses. It has built-in machine learning capabilities that will enhance perceptions and judgment. Companies can make necessary adjustments thanks to real-time data syncing and predictive analytics, which show potential business outcomes. To keep data processing and analytics in the same tool, BigQuery includes a BI engine. However, it also integrates with other BI tools, such as Google’s Data Studio and Looker.

Key takeaway: Google BigQuery is best for companies using Google’s Cloud Platform that want the ability to incorporate AI and ML into their decision-making process.

A serverless data warehouse solution called Google BigQuery aids in the increased agility of businesses. It has built-in machine learning capabilities to enhance perceptions and judgment. Companies can make necessary adjustments thanks to real-time data syncing and predictive analytics, which show potential business outcomes. BigQuery integrates with other BI tools like Google’s Data Studio and Looker in addition to having a built-in BI engine that keeps data processing and analytics in the same application.

Additional Question — What is a data warehouse software?

What is data warehouse in SQL?

Columnar storage is used by SQL Data Warehouse to store data in relational tables, which lowers data storage costs and enhances query performance. Data processing is distributed across multiple nodes using a scale-out architecture by SQL Data Warehouse.

What is ETL in data warehousing?

The data integration process known as ETL, or extract, transform, and load, brings together data from various data sources into a single, consistent data store that is then loaded into a data warehouse or other target system.

Is a data warehouse a database?

A data warehouse is a type of database that combines copies of transaction data from various source systems and makes them available for analytical use. The key difference is that data warehouses are built to handle the analytics needed to reduce costs and improve quality in the modern healthcare environment.

What is an example of a data warehouse?

Data warehousing combines information and data from various sources into a single, comprehensive database. An organization’s point-of-sale systems, mailing lists, website, and comment cards, for instance, could all be combined in a data warehouse to create customer information.

What is data warehousing system process?

Data warehousing is a technique used to compile and manage data from various sources into a single repository in order to generate useful business insights. It is easier to perform analysis and reporting at various aggregate levels when all of your data is in one location.

Is Snowflake a data warehouse?

Snowflake: A unique data warehouse architecture Designed with a patented new architecture to handle all aspects of data and analytics, it combines high performance, high concurrency, simplicity, and affordability at levels not possible with other data warehouses.

Is Snowflake an ETL tool?

Both transformations during (ETL) and after loading (ELT) are supported by Snowflake. Informatica, Talend, Fivetran, Matillion, and other data integration tools are just a few of the many that Snowflake works with.

Is Snowflake better than Azure?

Snowflake appears to be a better choice for very large data warehouses because it can scale up to the petabyte range and the storage costs are significantly lower. As a better alternative to Azure SQL DB for processing large amounts of data while remaining on the Microsoft Data Platform, Azure Synapse Analytics is available.

Why is Snowflake better than AWS?

Instead, AWS Snowflake makes use of a SQL database engine with a cloud-specific architecture. Snowflake is a lot faster, more adaptable, and user-friendly than conventional data warehouses.

What SQL does Snowflake use?

The most popular standardized version of SQL, ANSI, is supported by Snowflake, a data platform and data warehouse. As a result, Snowflake offers access to all of the most popular operations.

Is Snowflake like Azure?

Snowflake is a cloud-based data warehouse that can handle both structured and unstructured data. Snowflake, which debuted in 2014, is significantly more recent than Azure and SQL Database. The user base has grown to be devoted, though.

Dannie Jarrod

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