What is Data Warehouse?
The Data Warehouse (DW) or the Enterprise Data Warehouse (EDW) is the essential component for Business Intelligence (BI) systems, in which the process for assembling, administering, and manipulating the data from multiple varieties of data sources is performed in order to turn up with the significant business decision making measures, by using the EDW as a way to associate and analyze the data related to the business requirements for which the Business Intelligence is necessitated in the form of Reporting and Analysis.
It is considered one of the most essential and critical components of business intelligence. They are central repositories of integrated data that is obtained by more than one source. Current and historical data are stored in them in one place. This is used to create analytical reports for all the workers all through the enterprise. The data which is stored in the warehouse is uploaded from operational systems, which are generally marketing or sales. This data then passes through an operational data store and may require data cleansing to ensure that the right quality of data is being delivered before it is used in the EDW for reporting. Then comes the activity of ETL (Extract, Transform, Load), which makes use of staging, data integration, and access layers to make use of key functions.
If we try to understand the concept in very simpler terms, it means a system that is used to report and store data. The data initially is generated in multiple systems such as some form of RDBMS, Oracle, Mainframes, etc., then it is moved to the data warehouse for long-term storage and so that it can be used for analytical purposes. This storage is structured such that users from many divisions or departments of a single organization can access and analyze the data as per their own needs and requirements. These are analytical tools which are solely built to provide support in the decision-making process and a system for reporting to users for many departments. They are also archival data, consisting of historical usage data of the organization, which is specifically not maintained in operational systems. In essence, they are used to create a single version of truth for the entire organization.
How does it make working so easy?
It maintains the copy of information and data from source transaction systems. It also:
- Integrates data from multiple sources and puts it into one database or a model; therefore, a single query engine
can be used to put data in ODS (operational data store).
- Helps in mitigation of database isolation level lock problem, which was generally caused due by large, long-running, analytical queries.
- Data history is maintained even if the source transactional systems are not maintaining it.
- A central view across the enterprise can be seen once all the data is put from multiple resources.
- Code consistency and descriptions and even fixing bad data are improved. Basically impacts the overall data quality.
- Teradata: This company tops the list when it has to be about working with EDW technology. It brings about more than 30 years of history onto the table. The company has its own software Teradata which is used by most companies dealing with the data warehouse in their organizations, especially all the banks. This company always has some new innovations to bring to the table, including the latest Hadoop-based technologies.
- Oracle: This is the traditional company that is the first to strike the mind when we talk about relational databases. The 12c database has been unbeatable and is known for its high-performance standards, scale, and optimized data warehousing. The compression techniques are the new features provided by this company in the EDW space.
- Amazon Web services: This IaaS of Amazon in the space of cloud computing is about the whole transformation and migration of the data storage and warehousing onto the cloud has given data warehousing an entirely new definition.
- Cloudera: This has been among the best companies in the space of EDW and big data technology as it provides an EDH (Enterprise data hub) for the large variety of data store which focuses on batch processing. Their EDW is based on CDH.
- MarkLogic: This company provides a NoSQL database platform. This gave a new dimension as companies started to believe in the power of NoSQL after this company introduced it.
What can you do with a data warehouse?
- Statistical analysis
- Decision making
The raw data is firstly formatted, also called cleansing and normalizing, whereby it is processed and transformed according to the business requirement and removing the inconsistencies from the raw data. It is then stored in the EDW itself. Finally, an access layer allows the applications and tools to retrieve e data in a format suitable to their needs. There is another aspect to the architecture which covers the part related to metadata which scientists and engineers mainly use to collect information about the sources, naming conventions, refresh schedules, etc.
- Multiple source integration
- Performing new analysis
- Reduced cost to access historical data
- The standard single version of the truth
- Helps in improving turnaround time for data analysis and reporting
- Broad vision
- Communication skills
- Understanding of data and processes
- Ability to analyze
- General systems and application knowledge
Why should we use data warehousing?
We should use data warehousing to provide our organization with a single version of the truth with the required data and no other computing overhead over the processed transactional resources. OLAP will take care of the analytical processing part, and therefore the business insights and a meaningful generation of information can also be provided with the data warehousing.
The scope of data warehousing is in any domain that has something to do with analytics and in the cloud domain these days. You can become a DW engineer or a consultant or even make your seamless way into big data technologies. You can also look forward to being a data scientist. The scope of data is endless so is the scope for data warehousing.
Why do we need a data warehouse?
We need a data warehouse because it makes no sense whatsoever to use multiple source systems and not be able to instantly fetch all the required information. Also, if not accessed, the historical data doesn’t give many advantages to the organization as a whole. Therefore, generating meaningful information set from the raw data can be done using analysis and querying tools, and therefore data warehousing comes into the picture.
Who is the right audience for learning data warehousing techniques?
Anybody with the right mindset, broad vision, is good at data crunching, has good querying skills, is interested in data-related technologies, has good analytical skills is an ideal candidate to learn and start using data warehousing technologies.
How will this technology help in career growth?
This technology does the most critical part of any organization: data crunching and the ability to generate insights by analysis. Therefore, generating meaningful information from raw data can be achieved by using this technology. You can also look for transforming your way into a big data ecosystem and later data science if you are familiar with the base of it.
It has been the backbone of many organizations to date and will continue to be so. However, the domain and the definition are increasing with every passing day due to the emergence of so many new technologies and tools. Therefore, making your way into this space is one of the best decisions in analytics as this forms the base and helps you understand exactly how the data processing works and the background processes it is governed with. I hope you liked the article. Keep reading for more information.
This has been a guide to What is Data warehouse. Here we discussed the working, advantages, required skills, along with career growth in the data warehouse. You can also go through our other suggested articles to learn more –