Storage and Data Formats
Traditional data warehousing focused on relational databases as the primary data and storage format. A key concept of the data lake is the ability to reliably store a large amount of data. Such data volumes are typically much larger than what can be handled in traditional relational databases, or much larger than what can be handled in a cost-effective manner. To this end, the underlying data storage must be scalable and reliable.
The Hadoop Distributed File System (HDFS) has matured and is now the leading data storage technology that enables the reliable persistence of large-scale data. However, other storage technologies can also provide the data-store backend for the data lake. Open source systems such as Cassandra, HBase, and MongoDB can provide reliable storage for the data lake. Alternatively, cloud-based storage services can also be used as a data-store backend. Such services include Amazon S3, Google Cloud Storage, and the Microsoft Azure Blob Store.
Data Lakes: 8 Enterprise Data Management Requirements
Data Lakes: 8 Enterprise Data Management 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.
