If you are a Hadoop user, you might want to consider Azure Data Factory. Today, data is a serious problem for a number of different companies. Because data can be generated from so many different sources, such as smartphones, smart watches, tablets, computers, and more, the volume is astronomical. It is becoming quite a challenge. Azure Data Factory helps to provide users with a way of managing all of this.

What Does It Do?

The goal of the system is to create an easy way to collate and manage the data. It is capable of working on premise, as well as through cloud data sources and SaaS (software as a service) apps. You are able to collect data from a number of different sources and place them into a “data lake”. This is the location where all of the raw data is aggregated.

In today’s world, it is simply better to collect as much raw data as possible. This makes it easier to deal with the various business and data requirements as they present themselves. It is very helpful to have all of this data because the exact requirements are not often known by businesses when they are first starting out. By collecting all of the data and then determining how to best use it, you can ensure that you are not missing any important information and data.

How Can It Help?

The problem in the past has been connecting to the sources and then collecting all of that data into a single location so people can actually use and learn from that data. For example, businesses want to be able to collect customer data so they know what their customers are doing and what types of products and services those customers are buying. By aggregating the data into a form that’s actually usable, from across multiple sources, it then makes it possible for the companies to more easily recommend other services and products to those customers. It essentially makes the business/customer relationship stronger and more symbiotic. This is only one of the potential reasons to use Azure Data Factory with Hadoop.

By collecting the data in the lake, or hub as many call it, you can then utilize it for your business as needed. You are able to use Data Factory to take in the data, prepare it, transform it, and analyze it. You can also then publish the data, which can make it easier for the various segments of your business to know what’s happening with their customers, and how they might be able to change and improve based on the results of that data.

Utilizing this tool with Hadoop, or Azure HDInsight, provides you with a nice visual interpretation of your data across various segments. It makes monitoring very simple, and you are even able to set up monitoring alerts so you always know what is happening with your data. The visualization makes it easy to determine the sources of the data, and then to monitor all of it at once.

Let’s look at some of the ways that you can utilize Data Factory and Hadoop for your benefit. It’s important to keep in mind that all companies are different, and their needs will be different. Still, it’s possible to use these systems for any type of company that collects and uses data, which should be all companies out there today, regardless of industry or size.

As we mentioned, online retailers will find quite a bit to like about using Data Factory and Hadoop. It allows them to get a much better insight into just what their customers have bought, which lets them know what other products they might be interested in buying. This makes it easier to develop marketing programs geared toward that segment of their audience.

Other companies are using data gleaned and parsed through Data Factory and Hadoop to help them improve their overall marketing. It is possible to take in data from their last marketing campaign to see what works and what did not work, and then to make changes for follow up campaigns.

Data Factory uses several different elements that help to streamline the process. It is important to understand the basics how each of these works to understand how it might be able to help your company. These elements include activities, pipelines, datasets, and linked service (Hadoop in this case).

Activities refer to the actual actions that a company will perform on the data, and then produce datasets as the output. Some of the available examples include copying activities, which means you are copying data from one set to another. Another option is hive activity, which you can use on Hadoop to analyze or change the data.

A pipeline refers to a group of activities put together to perform some type of function. One of the common uses of this type of function is to clean out the log file data.

Datasets are reference points for that data that you will need to use for input our output on an activity. They help to identify the structure of the data that you’ve collected. These could include things such as files, documents, tables, and more.

Many customers are using Hadoop as their linked service. This allows them to connect data from a number of external resources.

It Makes Business Easier

As we’ve discussed, the sheer volume of data that’s coming into companies today is massive. There needs to be a better way to utilize and analyze all of this data, and the combination of Data Factory and Hadoop could be just the answer you need. Over the course of the coming years, there will only be more and more data to collect and analyze. Think about all of the new ways that people are shopping, searching, and being exposed to the web. It’s going to keep growing, and businesses need to take action now in order to stay on top of all of this data.

Resource: Azure Data Factory for the Hadoop User with Anand Subbaraj

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