Informatica Big Data Management Overview
Informatica Big Data Management enables your organization to process large, diverse, and fast changing data sets so you can get insights into your data. Use Big Data Management to perform big data integration and transformation without writing or maintaining Apache Hadoop code.
Use Big Data Management to collect diverse data faster, build business logic in a visual environment, and eliminate hand-coding to get insights on your data. Consider implementing a big data project in the following situations:
- •The volume of the data that you want to process is greater than 10 terabytes.
- •You need to analyze or capture data changes in microseconds.
- •The data sources are varied and range from unstructured text to social media data.
You can identify big data sources and perform profiling to determine the quality of the data. You can build the business logic for the data and push this logic to the Hadoop cluster for faster and more efficient processing. You can view the status of the big data processing jobs and view how the big data queries are performing.
You can use multiple product tools and clients such as Informatica Developer (the Developer tool) and Informatica Administrator (the Administrator tool) to access big data functionality. Big Data Management connects to third-party applications such as the Hadoop Distributed File System (HDFS) and NoSQL databases such as HBase on a Hadoop cluster on different Hadoop distributions.
The Developer tool includes the native and Hadoop run-time environments for optimal processing. Use the native run-time environment to process data that is less than 10 terabytes. In the native environment, the Data Integration Service processes the data. The Hadoop run-time environment can optimize mapping performance and process data that is greater than 10 terabytes. In the Hadoop environment, the Data Integration Service pushes the processing to nodes in a Hadoop cluster.
When you run a mapping in the Hadoop environment, you can select to use the Spark engine, the Blaze engine, or the Hive engine to run the mapping.
Example
You are an investment banker who needs to calculate the popularity and risk of stocks and then match stocks to each customer based on the preferences of the customer. Your CIO wants to automate the process of calculating the popularity and risk of each stock, match stocks to each customer, and then send an email with a list of stock recommendations for all customers.
You consider the following requirements for your project:
- •The volume of data generated by each stock is greater than 10 terabytes.
- •You need to analyze the changes to the stock in microseconds.
- •The stock is included in Twitter feeds and company stock trade websites, so you need to analyze these social media sources.
Based on your requirements, you work with the IT department to create mappings to determine the popularity of a stock. One mapping tracks the number of times the stock is included in Twitter feeds, and another mapping tracks the number of times customers inquire about the stock on the company stock trade website.