Below are the set of use cases which is widely using Hadoop and Spark Integration for the Bigdata processing in the industries .
Use Case 1: Data Lake
Building an “enterprise data hub” or “Data Lake.” The idea is you have disparate data sources, and you want to perform analysis across them. This type of project consists of getting feeds from all the sources (either real time or as a batch) and loading them into Hadoop Ecosystem. Sometimes this is step one to becoming a data driven product or sometimes you simply want pretty reports. Data lakes usually materialize as files on HDFS and tables in Hive or Impala. But new way of approach is required to get the maximum throughput because Hive is slow. It encourages the developers to build use cases in spark in top of Hadoop. We are the best Training institute for learning Hadoop Training in Chennai.
Use Case 2: Specialized analysis
Many data consolidation projects actually begin here, where you have a special need and pull in one data set for a system that does one kind of analysis. These tend to be incredibly domain-specific, such as liquidity risk/Monte Carlo simulations at a bank. In the past, such specialized analyses depended on antiquated, proprietary packages that couldn’t scale up as the data did and frequently suffered from a limited feature set (partly because the software vendor couldn’t possibility know as much about the domain as the institution immersed in it).
In the Hadoop and Spark worlds, these systems look roughly the same as data consolidation systems but often have more HBase, custom non-SQL code, and fewer data sources (if not only one). Increasingly, they’re Spark-based. We are the best Training institute for learning Hadoop Architect Training in Chennai.
Use Case 3: Complex event processing
Here we’re talking about real-time event processing, where sub seconds matter. While still not fast enough for ultra-low-latency (picosecond or nanosecond) applications, such as high-end trading systems, you can expect millisecond response times. Examples include real-time rating of call data records for telcos or processing of Internet of things events. Sometimes, you’ll see such systems use Spark and HBase.
In the past, such systems have been based on customized messaging software — or high-performance, off-the-shelf, client-server messaging products — but today’s data volumes are too much for either. Trading volumes and the number of people with cellphones have shot up since those legacy systems were created, and medical and industrial sensors pump out too many bits. I haven’t used it yet, but the Apex project looks promising and claims to be faster than Storm. We are the best Training institute for learning spark Training in Chennai.
Use Case 4: Streaming as ETL
Sometimes you want to capture streaming data and warehouse it somewhere. These projects usually coincide Data Lake and Specialized Analysis projects, but add their own scope and characteristics. (Some people think they’re doing Complex event processing, but they’re actually dumping to disk and analyzing the data later.) These are almost always Kafka and Storm projects. Spark is also used, but without justification, since you don’t really need in-memory analytics.
Use Case 5: Replacing the existing Analytical Platform like SAS
SAS is expensive and we’re not buying boxes for all you data scientists and analysts so that you can “play” with the data. Besides, you wanted to do something different than SAS could do or generate a prettier graph.
While I’ve seen other Hadoop, Spark, or Storm projects, these are the “normal,” everyday types. If you’re using Hadoop, you probably recognize them. Some of the use cases for these systems I’ve implemented years before, working with other technologies.
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