Many Hadoop users, seeking higher performance and a better analytics engine, are turning to Apache Spark for data transformation (ELT) on HDFS. While Spark offers many advantages, you still need programmers (Scala or Java) to create your jobs.
Mike Atkin of the EDM Council speaks eloquently about the "perfect storm" for data in Financial Services. Two converging forces, regulatory reporting requirements and the need for customer insight, are placing unprecedented demands on the data infrastructure in most financial institutions.
Data integration projects can be time consuming, expensive and difficult to manage.Traditional data integration methods require point to point mapping of source and target systems. This effort typically requires a team of both business SMEs and technology professionals. These mappings are time consuming to create and code and errors in the ETL (Extract, Transform, and Load) process require iterative cycles through the process.
A round up of recent industry news on the topics of Big Data and Enterprise Data Management
Driving business value from your data often requires integration across many sources. These integration projects can be time consuming, expensive and difficult to manage. Any short cuts can compromise on quality and reuse. In many industries, non-compliance with data governance rules can put you firm’s reputation at risk and expose you to large fines.
Happy New Year !!
Data Integration is one of those necessary but evil tasks that is part of almost every project - M&A, system consolidation, customer onboarding and regulatory reporting all require data to be moved, transformed or combined.