This paper aims at presenting the Big Data Integration and techniques available for Big Data Integration using Hadoop techniques. Data is collected and stored at unprecedented rates. Big data integration is the process of transferring data in source format into destination format. Many data warehouse and management are supported by integration techniques and transportation by using Extract Transform-Load process. Map Reducing method are used to extract and classify the data groups. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. Big Data Integration as a process is highly combined and iterative to add new data sources. The challenge is not only to store and manage the vast volume of data, but also to analyze and extract meaningful value from it. Big data comes from relatively new types of data sources like social media, public filings, and content available in the public domain through agencies or subscriptions, documents and e-mails including both structured and unstructured texts format. Keywords—Map Reduce Techniques, Hadoop, Fault-Tolerance and High Availability