Data ware house

Today’s technology is a booming market full of exciting and innovative products and new learning opportunities. With technology as a major consumer of our time and also an exciting experience in our world today, are we motivated to generate something new? Obviously, yes! So, today let’s get on to the topic of Data ware house.

Bill Inmon, the “Father of Data Warehousing,” defines a Data Warehouse (DW) as, “a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.” Basically, Data Warehouse is a data storage architecture which allows “business executives to systematically organize, understand, and use their data to make strategic decisions.”

Let’s have a look at the main characteristics of the Data ware house:

The Data Warehouse is designed to analyze data. The ability to define a Data Warehouse by subject matter makes the Data Warehouse subject-oriented.

Integration is closely related to subject orientation. Data Warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure.

Nonvolatile means that, once entered into the warehouse, data should not change. The purpose of a warehouse is to enable us to analyze what has occurred.

A Data Warehouse’s focus on change over time is what is meant by the term time-variant.

The evolution of Data ware house:

Classic analytical processing of transaction-based data is done in the Data Warehouse as it has always been done. But now analytics on contextualized data can be done, and that form of analytics is new and novel. Most organizations have not been able to base decision-making on unstructured textual data before. And there is a new form of analytics that is possible in the Data Warehouse, which is the possibility of blended analytics. Blended analytics is analytics done using a blend of structured transactional data and unstructured contextualized data. Predictive and Prescriptive Analytics, as well as various Machine Learning technologies and others are changing the way data is managed and analyzed. Data Warehouse has a strong future in the new world of Big Data and Advanced Analytics.

Let’s now get on to the point as to why Data ware house is crucial:

Data ware house helps organizations to take “smarter and quick decisions” on reducing costs and to increase the revenue, by comparing the reports to improve their performance.

Data warehouse maintains both “current data and historical data” for analytical reporting and fact-based decision making.

Data warehouse gathers all the operational data from several heterogeneous sources of “different formats” and through the process of extract, transform and load (ETL) it loads the data into DW in a “standardized dimensional format.”

We have three main types of DW Applications:

  • Information processing
  • Analytical processing
  • Data mining which serves the purpose of BI

The key advantages to the organizations when the Data ware house system is productive are:

  • Enhanced Business Intelligence
  • Increased System and Query Performance
  • Business Intelligence from Multiple Sources
  • Timely Access to Data
  • Enhanced Data Quality and Consistency
  • Historical Intelligence
  • High Return on Investment

Snowflake and the future of Data ware housing:

The future of DW lies in serverless infrastructure. On-premise servers and hardware are becoming antiquated, and as their presence diminishes, the constraints and typical difficulties of acquisition and management go with them. Snowflake has proven to be one of the most compelling players in the game as an up-and-coming leader in for Data Management Solutions for Analytics.

It has gained traction with companies of all industries because they’re modernizing Data Warehousing-as-a-Service (DWaaS) with real-time analytics in a unique virtual warehouse. The major attraction is its simple, yet powerful, three-layer architecture of database storage, query processing, and cloud services with the ability to scale independently. Platforms like Snowflake are revolutionizing the way data is stored and consumed.

Whether you’re transitioning between DW service providers or transferring an on-premise DW to a virtual environment, both processes require rigorous planning, concise strategy, and efficiency. At DAKSYAM, we’re well-versed in nearly every DW platform, as well as the implementation and migration of DWs, small and large. If you’re looking to change the course of your DW initiatives, reach out to us today!

Question and Answer section:

1) What makes a good data warehouse?

A cost-effective data warehouse should be able to scale compute capacity to match demand, and then quickly and easily scale back when usage decreases. The cloud can help solve this problem, but only if the underlying architecture of the warehouse supports it.

2) What are the types of data warehouse?

Three main types of Data warehouses are Enterprise Data Warehouse (EDW), Operational Data Store, and Data Mart.

3) What is meant by data warehousing?

Data warehousing is the electronic storage of a large amount of information by a business or organization. A data warehouse is designed to run query and analysis on historical data derived from transactional sources for business intelligence and data mining purposes.

4) Why Snowflake is special?

It has out-of-the box features like separation of storage and compute, on-the-fly scalable compute, data sharing, data cloning, and third party tools support.

5) Why is Snowflake better than AWS?

With Snowflake, compute and storage are completely separate, and the storage cost is the same as storing the data on S3. AWS attempted to address this issue by introducing Redshift Spectrum, which allows querying data that exists directly on S3, but it is not as seamless as with Snowflake.