#anaytics

Bad dashboards aren’t just annoying — they’re expensive. In mining and operations, every delayed decision carries a cost. source

Modern applications produce large numbers of events. These events can be users clicking, IoT sensors accumulating data, network events or log messages. There is a lot of requirement to analyze the data as it comes and compare it with historical data to figure out anomalies and patterns.

Stream-processing vs Analytics

Why would you need Druid?

Stream processing technologies help to look…

The world of data has been growing exponentially, and the data management industry is totally changed from what it was a few years ago. Around 90% of the current data has been generated in the last couple of years only. According to a report by Domo, our continuous data output is nearly 2.5 quintillion bytes in a day, which means there’s massive data generated every minute. With technological transformation, data has become a critical factor in business success. Above all, processing data in the right way has become a pivotal solution for many businesses around the globe.

If we look back, terms like a data lake, ETL (Extract, Transform, Load), or warehousing would have been baffling for most people, or merely some buzzwords.

Today, data management technology is growing at a fast pace and providing ample opportunities to organizations. Organizations these days are full of raw data that needs filtering. Systematically arranging the data to get actionable insights for decision-makers is a real challenge. Thus, meaningful data accelerates decision-making, and using ETL tools for data management can be helpful.

The evolution of Extract Transform Load (ETL)

Data warehouses and ETL tools were created to get actionable insights from all your business data. There are currently several ETL tools in the market that have expanded functionality for data cleansing, data profiling, big data processing, master data management, data governance, and Enterprise Application Integration (EAI). With the availability of data in the warehouse or Online analytical process (OLAP) cube, a Business Intelligence (BI) software is used to visualize and analyze them. This software helps with reporting, data discovery, mining, and boarding.

The complete process

The process of extracting and organizing raw data, transforming to make it understandable, and loading it into a database or a data warehouse to easily access and analyze it, is known as an ETL process. In short, it’s an essential component in the data ecosystem of any contemporary business.

Since data coming from various sources has a distinct structure, every dataset needs to be transformed differently before using it for business intelligence and analytics. For example, if you organize data from source systems like Google Analytics and Amazon Redshift, these two sources should be treated separately with the whole ETL process.

Implementing the ETL process in the data warehouse

The ETL process includes three steps:

1.    Extract

This step comprises data extraction from the source system into the staging area. Any transformations can be done in the staging area without degrading the performance of the source system. Also, if you copy any corrupted data directly from the source into the database of the data warehouse, restoring could be a challenge. Users can validate extracted data in the staging area before moving it into the data warehouse.

The data warehouses should merge systems with hardware, DBMS, OS, and communication protocols. Sources include legacy apps like custom applications, mainframes, POC devices like call switches, ATM, text files, ERP, spreadsheets, data from partners, and vendors. As a result, you need a logical data map before extracting data and loading it physically. The data map represents the connection between sources and target data.

Conclusion

Every business around the world, whether small, mid-sized, or large, has an extensive amount of data. However, this data is nothing without using a robust process to gather it. Implementing ETL in data warehousing provides a full context of your business for the decision-makers. The process is flexible and agile that allows you to swiftly load data, transform it into meaningful information, and use it to conduct business analysis.

An advanced data warehouse, also known as an enterprise data warehouse, serves as a data hub for business intelligence. It is a support system that stores data across the organization processes it, and enables it to be utilized for various business purposes, including reporting, business analysis, and dashboards. A data warehouse system stores structured data from multiple sources such as Online Transaction Processing (OLTP), Customer Relationship Management (CRM), and Enterprise Resource Planning (ERP).