Table of contents

Introduction

Operationalizing Big Data

2.1.

Using analytics in daily workflow

2.2.

Operationalization progress

Big Data Integration

Frequently Asked Questions

Conclusion

Last Updated: Mar 27, 2024

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Integrating big data

Author vishal teotia

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Introduction

Integrating Big Data is an essential and essential step in any project involving Big Data. There are, however, several issues to take into consideration. Big Data Integration combines data from several different sources and software formats, then provides users with one unified view of the resulting data accumulated.

Traditionally, data integration techniques mainly entailed using an ETL (extract, transform and load) process. ETL involves ingesting and cleaning data, then putting it into a data warehouse. Traditional data integration is like a glass of water, while big data integration resembles a smoothie.

Operationalizing Big Data

Decision-makers can use big data analytics for more than just delivering reports. In addition, it can help a company's day-to-day work. Big data analytics is no longer a nice thing for enterprises: It's now mission-critical.

In 2019, Veritas said, "In just a few years, big data has been advanced from the scattered experimental projects to achieve mission-critical status in digital enterprises, and its importance is increasing. According to IDC, organizations able to analyze all relevant data and deliver actionable information will earn $430 billion more than their less analytically oriented peers. Once performed on an occasional basis, big-data analytics are now performed daily at many enterprises, including Amazon, Walmart, and UPS."

The key to operationalizing big data is to get it out of the test sandbox and into the business. The most active roles for the big data in the industry have been in decision support.

Medical practitioners use diagnostic analytics systems with machine learning to determine the best diagnosis and course of treatment for specific conditions.
Retailers can gain insight into consumer buying patterns from web-based data about which products and brands are moving the most, who is buying them, and where they are being purchased.
Tram tracks and parts of tram equipment are equipped with sensors that indicate which areas need immediate or near-term repairs for the system not to fail.

The examples above all illustrate the first tier of big data analytics deployment. They use big unstructured data and create static reports for managers that can be acted upon.

Using analytics in daily workflow

However, when big data analytics are fully operationalized, there is a second-tier stage of engagement where firms integrate big data analytics directly into the daily workflows of their businesses. Using the data gleaned from analytics, these companies are not only able to make better decisions, but they're also able to automate specific company tasks.

Decision-making in banking is an excellent example of system automation in operations. In the past, software programs would assess a loan applicant's creditworthiness and determine a "lend" or "don't lend" decision. The applicable loan rate was based on the loan applicant's credit status, the loan size, and the loan's level of risk. The lending supervisor has the final say, but in actuality, the lending software has made the decision.

Operationalization progress

When it comes to progress in operationalizing Big Data, there

are only slight differences across industry sectors, suggesting

there is widespread recognition of the value of Big Data, see

below.

Big Data Integration

Compared to traditional relational databases, elements of the big data platform manage data in new ways due to the need for scalability and high performance when managing structured and unstructured data. Each component of the big data eco-system, from Hadoop to NoSQL Databases, is unique in how it extracts, transforms, and loads data.

Additionally, the traditional ETL tools are adapting to cope with the new characteristics of big data. The conventional integration methods take on new meanings in the world of big data, and the integration technologies require a platform that enables data quality and profiling.

Big data integration can be done in Real-time or with batch processing (data on the rest), while traditional data integration is performed with batch processing (data on the rest). As a result, the ETL phases are sometimes reordered into ELT, so data is extracted, loaded into distributed file systems, and then transformed before being used.

To make sound business decisions based on big data analysis, data needs to be trusted and understood at all levels of the organization. Enterprise-wide, it must be delivered in a trusted, controlled, consistent, and flexible way.

To accomplish this goal, three basic techniques are used:

Schema Mapping
Record Linkage
Data Fusion

Frequently Asked Questions

1. What are the Five V’s of Big Data?

The five V’s of big data are Variety, Volume, Veracity, Velocity, and Value.

2. List some challenges that come with Big Data.

Big data has many problems and challenges, such as capturing, searching, analyzing, transferring, and extracting valuable insights from big data.

3. What is ETL?

ETL stands for extract, transform and load.

4. Example of Data Integration Tools?

Conclusion

The data in the world is growing at a swift pace. Integrating Big Data is now mission-critical. While still at a relatively early stage of adoption, implementing Big Data initiatives is beginning to pay off for early adopters. Organizations that profit from Big Data are significantly more likely to be agile and insights-driven. They quickly modify their Big Data strategy to meet changing business needs as they build on early successes and evolve into insights-driven organizations.

Check out this link if you want to explore more about Big Data.

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