At first, Hadoop was designed to make easy affordable to store and process petabytes of structured, unstructured and semi-structured data, for example, clickstream data, financial ticker data, system logs and sensor data, generated at incredible speeds and written thousands of times per second. Once written to Hadoop, it transforms as “data-at-rest” that is retrieved and analyzed later using batch applications built with MapReduce that run for hours, or with NoSQL databases for interactive access.
Despite all, the ultimate advantages of big data are lost if fresh, fast-moving data is dumped into the Hadoop Distributed File System (HDFS) instead of analyzed as it happens, the reason is that, the ability to act now is lost. Contrary to this, fast data is about “data-in-motion” that demands an immediate response and action.
Data-in-motion and data-at-rest, contrary yet complimentary to each other
On the other hand collection process of both data-in-motion and data-at-rest are same otherwise, but the primary difference is the analysis occurs in real time as data is generated and captured. Businesses can respond promptly to changing market conditions and can take advantage of big data velocity and make decisions with a direct impact on the bottom line. Additionally, as most businesses are going global, the potential to react immediately to information generated at one and the same time from multiple locations worldwide without downtime is essential to competitive advantage.
It clearly says that, data-in-motion and data-at-rest are mutually exclusive, but contrary to it they are complimentary to each other. Data-at-rest gives you historical context, while data-in-motion clarifies you what’s happening now. With the help of both of them in combination, the two enables decisions to be made in real time within a historical context, instead of a single-point-in-time vacuum.
Spark is the necessary toolset to handle
What is necessary is a toolset that handles both data-at-rest and data-in-motion, and there is for this an option who serves for it, that is Spark, which is the toolset of choice. Its in-memory framework allows it to handle streaming data in real time before it’s written to HDFS, with this, process data-at-rest at speeds up to 100 times faster than MapReduce to perform regression analysis that highlights patterns in historical data. Additionally, it has its own high-performance SQL engine for interactive queries against HDFS, also it can write to HDFS and other data stores too. With the help of Spark framework and the data visualization and analytics applications that work with it, businesses can react speedily to changing market conditions, take advantage of big data velocity, and make informed in-context decisions based on historical data to instantly impact the bottom line.
A hybrid approach is needed
Efficacious way to get benefits of fast data lies in a hybrid approach that includes active-active replication to integrate in-house big data deployments running any Hadoop distribution—Cloudera’s Distribution Including Apache Hadoop (CDH), Hortonworks Data Platform (HDP), MapR, Pivotal, IBM, Apache—on HDFS or any Hadoop Compatible File System (HCFS) with cloud-based Spark-as-a-Service entitle real-time analytics applications. If it is there, then there will be no need of Spark-trained staff and additional hardware and other infrastructure to help. There is one more benefit of it and that is, it allows swift movement of raw data as well as the end results into and out of the cloud in near-real time, without any disruption to existing on-premises Hadoop operations.
Global organizations have to gain real competitive advantage from fast data, for that there are two requirements, by Spark as a Service and that is downtime and data loss and the ability to handle huge volumes of data generated continuously across multiple locations.
For any application having stringent service-level agreements (SLAs) and regulatory compliance mandates associated with it, is difficult to eliminate downtime and data loss. When it comes to fast data applications, the negative impact of downtime and data loss is orders of magnitude greater due to the increased risk of missed opportunity.
As a by-product of ensuring data consistency across clusters and data centers, true peer-to-peer active-active replication would give continuous recent backup by default to protect against data loss. It would hold this to provide automated failover and recovery for the lowest-possible recovery point objective (RPO) and recovery time objective (RTO) to terminate downtime.
If sometime, unfortunately, a cluster or an entire data center went offline due to either scheduled maintenance, or hardware and network failures still users would have read/write access to their data at other locations because of an active-active solution. If something like an outage happens, just after that, resynchronization of clusters that were offline would happen automatically, decreasing the risk of human error during recovery.
Ability to process and analyze massive volumes of data
To reduce the time spend on these things, organizations should be able to process and analyze massive volumes of data generated on a 24×7 basis wherever it originates. These capabilities prove very beneficial when dealing with industrial sensor data and other Internet of Things use cases where data is generated everywhere and timeliness and accuracy are crucial. Now, to do all these, organizations should keep in mind following points,
First, is to confirm, critical, time-sensitive information is processed close to the source, without the delay and risk involved in moving data over a write pipeline for analysis. Second, copy it to other locations as needed at the same time it’s analyzed and ingested at the point of origin. And third is to, Combine in-house big data-at-rest that provides historical context with fast data-in-motion, so informed, real-time decisions can be made with an instant positive impact.
Hadoop and Sparks amalgamation do have a power to change IT.