Big Data Pipeline Challenges
Data, the new currency of the digital economy, is an untapped valuable resource that organizations should learn to extract and use to reap huge rewards. Today, organizations are struggling to cobble together different open source software to build an effective data pipeline that can withstand the volume as well as the speed of data ingestion and analysis.
To stay competitive in today’s global economy, organizations need to harness data from multiple sources, extract information and then make real-time decisions. Organizations are looking to build a Data Pipeline, an automated process that executes at regular intervals to ingest, cleanse, transform and/or aggregate the output dataset in the format that is suitable for downstream.
Operationally you can think of a big data pipeline, as a cluster of applications that enable the extract-load-transform-view data-process. The applications used within the big data pipeline differ from case to case and almost always present multiple challenges.
Creating an elastic, agile, & high-performance Big Data pipeline rapidly
Robin’s Application Virtualization Platform provides a complete out-of-the-box solution for hosting all the components in your big data pipeline created out of your existing hardware – proprietary / commodity, or cloud components. The solution can be deployed on bare metal or on virtual machines, allowing organizations to rapidly deploy multiple instances of their data-driven applications without creating additional copies of data.
Simple pipeline application & data management
Cloudera Cluster deployment on Robin
Robin’s application-aware fabric controller simplifies deployment and lifecycle management using container-based “virtual clusters.” Each cluster node is deployed within a container, and a collection of containers running across servers form the “virtual cluster.” This allows Robin to automate all tasks pertaining to the creation, scheduling, and operation of these virtual application clusters, to the extent that an entire data pipeline can be provisioned or cloned with a single click and minimal upfront planning or configuration.
Elastic Scaling and Cloning
Scaling and cloning of an ELK cluster
It is necessary to scale up or out as demand for resources spikes and then comes back to normal. Robin enables you to scale up with a single click by allocating more resources to the application. Robin enables you to scale out easily when you need to add nodes and helps you clone parts of your data when you need to give data to developers and analysts for analytics, test upgrades, testing changes or for integration testing.
Cluster Consolidation and QoS
Share data across 2 Cloudera clusters
Robin eliminates cluster sprawl by deploying a data pipeline on shared hardware. This also results in better hardware utilization. The key to successful multi-tenancy is the ability to provide performance isolation and dynamic performance controls. The Robin application-aware fabric controller equips each virtual cluster with dynamic QoS controls for every resource that it depends on – CPU, memory, network and storage. This creates a truly elastic infrastructure that delivers CPU, memory, network and storage resources – both capacity and performance – to an application exactly at the instant it is needed.
Application Time Travel
Robin provides out of the box support for application time travel. Cluster level distributed snapshots at pre-defined intervals can be really useful to restore the entire pipeline or parts of it if anything goes wrong. Robin recommends admins to take snapshots before making any major changes. Whether you are upgrading the software version or making a configuration change, make sure to have a snapshot. If anything goes wrong, the entire cluster can be restored to the last known snapshot in a matter of minutes.