Data Lakes: 8 Enterprise Data Management Requirements

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Storage and Data Formats

Unlike relational databases, Big Data storage does not usually dictate a data storage format. That is, Big Data storage supports arbitrary data formats that are understood by the applications that use the data. For example, data may be stored in CSV, RCFile, ORC or Parquet, to name a few. In addition, various compression techniques -- such as GZip, LZO, and Snappy -- can be applied to data files to improve space and network bandwidth utilization. This makes data lake storage much more flexible. Multiple formats and compression techniques can be used in the same data lake to best support specific data and query requirements.

2016 is the year of the data lake. It will surround and, in some cases, drown the data warehouse, and we'll see significant technology innovations, methodologies and reference architectures that turn the promise of broader data access and Big Data insights into a reality. But Big Data solutions must mature and go beyond the role of being primarily developer tools for highly skilled programmers. The enterprise data lake will allow organizations to track, manage and leverage data they've never had access to in the past. New data management strategies are already leading to more predictive and prescriptive analytics that are driving improved customer-service experiences, cost savings and an overall competitive advantage when there is the right alignment with key business initiatives.

So whether your enterprise data warehouse is on life support or moving into maintenance mode, it will most likely continue to do what it's good at for the time being: operational and historical reporting and analysis (a.k.a. rear-view mirror).

As you consider adopting an enterprise data lake strategy to manage more dynamic, poly-structured data, your data integration strategy must also evolve to handle the new requirements. Thinking that you can simply hire more developers to write code or rely on your legacy rows-and-columns-centric tools is a recipe to sink in a data swamp instead of swimming in a data lake. In this slideshow, Craig Stewart, VP product management at SnapLogic, has identified eight enterprise data management requirements that must be addressed in order to get maximum value from your Big Data technology investments.

 

Related Topics : APC, Resellers, Data Replication, Extract Transform and Load, Structured Data Integration

 
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