Gizmeon realizes the potential of SPARK and importance of Apache SPARK development as a Big Data Company. Gizmeon has a dedicated team of SPARK Developers working for us
What is Big Data Analytics?
Big data analytics examines huge volume of raw data to uncover hidden insights and patterns. Raw data gathered from a wide variety of internal data sources like sales transaction records, mobile and web analytics data, sensor data are correlated with external sources like social media channels, weather, geography to generate actionable customer intelligence.
Why is big data analytics important?
Big data analytics helps organizations to identify new opportunities, improve marketing initiatives, create targeted campaign offers, and identify new revenue opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.
Why traditional systems can’t handle this?
Consider the sheer volume and different formats of the data that is collected across different input sources like twitter feeds, website click information, transaction data, online website crawl data. Traditional data warehouses can’t cost effectively store, correlate and process huge volume of unstructured data from this kind of multiple sources.
As a result, newer, bigger data analytics environments and technologies have emerged, including Hadoop, MapReduce, Spark and NoSQL databases. With today’s technology like Apache Spark, it’s possible to analyse and correlate huge volume of your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.
Apache Spark is the answer for all your Big Data Analytics problems
Over the past couple of years, Hadoop MapReduce has become the dominant paradigm for big data processing but MapReduce is batch oriented in nature. So, any frameworks on top of it like Hive and Pig are also batch oriented in nature. For iterative processing as in the case of Machine Learning, Predictive analysis and interactive analysis, Map reduce is not the right fit.
Even though Spark is a relatively young when compared to MapReduce, it provides:
Run programs up to 100x faster than Hadoop MapReduce in Memory, or 10x faster on disk. Helps to generate quicker insights and less cluster utilization time there by having lesser operational costs and faster results.
Ease of Use
Write applications quickly in Java, Scala, Python, R. More developer friendly and nicer abstractions and APIs, thereby reducing development cost.
Combine SQL and complex analytics. Spark provides ability to run SQL queries on top of your big data. This also helps in easy migration of your exiting traditional ETL jobs to Spark and gain the benefits of Big Data like lower costs on storage, horizontal scalability on processing and faster in memory processing.
Spark runs on Hadoop, Mesos, standalone or in the Cloud. It can access diverse data sources including HDFS, Cassandra, HBase, Couchbase, Elastic Search and S3.
Streaming Data Analytics
Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams like Twitter streams, facebooks updates, IoT data (sensor data)
Machine Learning Library
MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy
With today’s technology like Apache Spark, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions
How Big Data Analytics are performed ?
Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Traditional systems may fall short because they’re unable to analyze as many data sources.
Sophisticated software programs are used for big data analytics, but the unstructured data used in big data analytics may not be well suited to conventional data warehouses. Big data’s high processing requirements may also make traditional data warehousing a poor fit. As a result, newer, bigger data analytics environments and technologies have emerged, including Hadoop, MapReduce and NoSQL databases. These technologies make up an open-source software framework that’s used to process huge data sets over clustered systems.
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