As it has been known that technology is advancing at a greater pace, the whole surroundings are getting digitalized which increases the dependency on data. This increased dependency on the data is the main reason for the introduction of a Data Warehouse. Here in the blog, we are going to discuss Data Warehouse and various tools associated with it.
Have a look at the points to be covered in this very blog and prepare to learn something new.
Table of Content
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What is Data Warehouse?
This is the first section of the blog, where we will discuss Data Warehouse (DWH). A data warehouse refers to a type of data management system that happens to be a collection of different types of data, which is associated with various software tools that are used to analyze and manage large volumes of data to gain better business intelligence.
- Data warehouses are analytical tools created to assist in decision-making for reporting users across numerous departments.
- Data warehouses gather historical commercial and organizational information so that it may be analyzed and new conclusions can be made.
- Data warehouse aids in creating a solitary, consistent system of reality for the whole corporation.
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In the blog’s previous section, we have just understood the working of a data warehouse and a basic understanding of how to select DWH tools and many more. Check out the well-curated list of data warehouse tools below.
Teradata
- Teradata is an international company based in Ohio, USA which is famous for its database solutions.
- Teradata’s DWH solutions are used by the majority of competitive enterprises for insights, analytics, and decision-making.
- Teradata is one of the most highly regarded Relational Database Management systems, it builds huge data warehousing applications that can use it.
- Teradata uses parallelism to make data warehouse solutions possible. The architecture of the Teradata database system is designed for massively parallel processing (MPP).
- The Teradata system divides the load across various processes and executes them concurrently to lessen workload while also ensuring that the task is completed successfully and fast.
- Regardless of the size of the query, Teradata processes 100% of the relevant data to deliver real-time, intelligent answers.
- Teradata is capable of consuming, processing, and managing data, which satisfies all integration or ETL requirements.
- Teradata provides a very intuitive graphical user interface to you which is easily usable with a minimal amount of training.
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Cloudera
- Cloudera offers software and services based on Apache Hadoop. Cloudera is a US-based software solutions provider, it was available for use in 2009.
- The Cloudera website offers a free download of it. The free edition has limited functionalities, but technical support is absent in the free edition.
- Only Cloudera additionally provides a cutting-edge enterprise platform, tools, and expertise that assists you by using Machine Learning and AI to uncover business understanding.
- Cloudera’s cutting-edge platform for Machine Learning and analytics provides you to develop and deploy AI solutions at scale, with effectiveness, and firmly, anywhere you would like, thanks to that is tailored and made for the cloud.
- The enterprise version CDH (Cloudera Distribution containing Apache Hadoop) comes in three editions:
Snowflake
- Snowflake is a type of data warehouse tool that is cloud-based and the foundation of Snowflake is based on Amazon Web Services and Microsoft Azure.
- You can utilize and pay for storage and calculations separately, thanks to the Snowflake design, which can expand storage and computation independently.
- Data processing is made easier with Snowflake because, you can combine, analyze, and transform data against a variety of data formats using just one language that is using SQL.
- Snowflake provides scalable, dynamic computing capacity with usage-based fees as its primary pricing model.
- With Snowflake, compute and storage are completely separable, and the storage benefit is identical to that of Amazon S3 storage, and Redshift Spectrum.
- With Snowflake, you can quickly, and without taking up more space, duplicate a table, a schema, or even a database.
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Google Data Warehouse Tools for developing machine intelligence-enhanced applications and transforming data into useful insights include:
Google BigQuery –
- Particularly Google BigQuery is famous for its capacity to manage a variety of complicated business use cases.
- Google BigQuery is a commercial-grade, cloud-based data warehouse solution.
- By storing and querying massive datasets quickly using super-fast SQL searches against multi-terabyte datasets, the platform offers users real-time data insights.
Cloud Dataprep
- It allows users to explore, clean, and prepare both structured and unstructured data.
- No infrastructure is needed to deploy or manage Dataprep because it is serverless and grows to any size.
There are other tools provided by google data warehouse tools namely Google Data Studio, and Dataflow.
SAP Data Warehouse Cloud
- SAP data warehouse cloud is a type of data management platform that is used by the organization for mapping business operations.
- Based on the reviews received by the organizations it is the best tool available in the domain.
- Solutions generated by the SAP data warehouse cloud are highly adaptive and flexible. Modularity was followed at the time of the development of the SAP data warehouse cloud.
- It is a premium standard application that includes both transactions and analytics in the data system.
Microsoft Azure Data Warehouse Tools provides various types of data warehouse services below mentioned are a few of them.
MS Azure SQL Database
- Azure SQL Database is a good option for Data Warehousing applications with up to 8 TB of data volumes and a sizable number of active users.
- It is a Platform as a Service (PaaS) database engine that is fully managed and handles all database management duties, such as patching, upgrading, backing up, and monitoring.
MS Azure Synapse Analytics
- Microsoft Azure Synapse Analytics includes data integration, big data analytics, and enterprise data warehousing.
- Machine learning technologies are used in their applications, which draw important conclusions from any data. Azure expedites project development by offering a complete analytics solution.
- Using the most recent privacy and security technology on the market, the data is completely protected.
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Oracle Autonomous Data Warehouse
- Data protection, data warehouse development, and the creation of data-driven applications are all handled by Oracle Autonomous Data Warehouse, a cloud-based data warehouse service.
- This technology automates the setup, safeguarding, regulating, scaling, and backing up of data within the Data Warehouse.
- To increase the productivity of analysts, data scientists, and developers, many self-service solutions have been implemented.
- IBM is a top option for large business clients because of its vast install base and selection of Data Warehouse and Data Management solutions.
- The company is well-known for its vertical data models, in-database analytics, and real-time analytics, all of which are crucial for data warehousing.
- Some of the prominent examples are IBM Db2 Warehouse, IBM Datastage, and many more.
Amazon Web Services
It is a well-known innovator in data warehousing solutions. AWS has added numerous services over the years, making it a platform that is both affordable and extremely scalable, let’s check out the different types below.
Amazon RedShift
- If your business wants very advanced capabilities, you have the money for a high-end tool, and you have an internal team competent in handling AWS’s vast menu of services, you may consider Amazon Redshift.
- Exabytes of structured, semi-structured, and unstructured data from the Data Warehouse, operational data stores, and a data lake may be queried using SQL using AWS Redshift.
Amazon S3 (Amazon Simple Storage Service)
- It is an object storage solution that enables infinite data storage and remote data retrieval.
- S3 is a low-cost storage option with performance, security, and scalability that are unmatched in the industry.
Amazon RDS (Amazon Relational Database Service)
- RDS is a cloud data storage service offered by AWS that enables the operation and scalability of relational databases.
- You can build a relational database that meets industry standards and manage all database maintenance tasks using its scalable and affordable technologies.
MarkLogic
- With its roots in XML databases, MarkLogic is a multi-model NoSQL database that has expanded to natively store JSON files and RDF triples for its linguistics data model.
- With its distributed architecture, it can manage many terabytes of data and billions of documents.
- The planning ethos that guided MarkLogic’s development asserts that information storage is only a component of the solution.
- Data models of MarkLogic are created using XML and JSON documents, which are then stored in a transactional repository.
- Due to the document structure, it also indexes the words and values from each of the loaded documents.
- A suite of tools called MarkLogic Data Hub helps users easily create an operational information hub on the MarkLogic Server.
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That was it, reader. We have covered all the important tools needed to be in the field, and I think our agender has been completed. Let’s jump to the conclusion and end this blog.
Conclusion
Congratulations on finishing; you now have a foundational knowledge of data warehouses and the related DWH tools.
Additionally, you learned about some critical parameters to consider when choosing the best data warehousing tool. In addition, this blog offered you a thorough overview of 10 widely used data warehouse tools. You can choose from a variety of data warehouse tool alternatives.
At last, this blog also emphasised the necessity of thoroughly evaluating the organizational requirements and needs before choosing any tool. The data warehouse is crucial to any firm in any industry because it serves as the central repository, making the right tool selection essential.