Scheduling and Workflow
Orchestration in the data lake is a mandatory requirement. Scheduling refers to launching jobs at specified times or in response to an external trigger. Workflow refers to specifying job dependencies and providing a means to execute jobs in a way that the dependencies are respected. A job could be a form of data acquisition, data transformation, or data delivery. In the context of a data lake, scheduling and workflow both need to interface with the underlying data storage and data processing platforms. For the enterprise, scheduling and workflow should be defined via a graphical user interface and not through the command line.
The open source ecosystem provides some low-level tools such Oozie, Azkaban, and Luigi. These tools provide command-line interfaces and file-based configuration. They are useful mainly for orchestrating work primarily within Hadoop.
Commercial data integration tools provide high-level interfaces to scheduling and workflow, making such tasks more accessible to a wider range of IT professionals.
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.
