Big data is all about gaining insights from large datasets, which in many cases cannot be processed by traditional relational databases says Peter DeCaprio. The ability to collect and store so much information in a centralized database makes big data technologies an ideal way to analyze it for business intelligence.
As the field of big data has gained popularity, dozens of tools have emerged that provide scalable solutions for collecting, storing, processing, and analyzing massive amounts of information. Here are 10 that demonstrate great potential for organizations looking to implement big data analytics into their businesses.
SAS Big Data Overview
Apache Hadoop provides a framework for running applications on clusters built from commodity hardware. It allows users to write applications in Java by taking advantage of its native for distributed computing.
The Apache Hadoop framework is composed of a distributed file system and a processing system that works on the files. The single master server manages the slave nodes via Apache Zookeeper, or it can also be backed by an additional Name Node to provide Namespace and DataDir-fragment replications.
The Hadoop core components are:
1) HDFS:
This is a Java-based programming interface with commands in NCDU/C++ for developing applications to work on top of the Hadoop File System.
2) Map Reduce:
This is a parallel data processing technique built with two phases, mapping and reducing functions. In phase one; input data from the HDFS will divide into smaller portions where each portion will be assigning to a different core for processing. The data will then be passed through the reducing phase where jobs perform on each smaller piece of data till it converges back to the overall dataset says Peter DeCaprio.
3) YARN:
This is an application management layer that allows multiple applications or frameworks to run on top of Hadoop. It provides services like resource management and scheduling, service monitoring, low-level task-execution support, and job/framework life cycle management capabilities.
4) Avro:
This is a serialization system with an RPC framework base on JSON data format. It uses schema definitions at runtime which offers typing systems and dynamic discovery along with binary formats similar to Thrifts without needing code generation.
Horton works is the only one that delivers 100% open source Apache Hadoop for enterprise, through its support of YARN, HDFS, and HBase. Horton works Data Platform (HDP) also includes Hive for SQL on Hadoop, Ambari for cluster management, Pig for data flow programming language, Flume to move large volumes of log files into HDFS, and Sqoop to move data between relational databases and HDFS.
Cloudera delivers an enterprise-ready commercial distribution that simplifies deployment and also operations with secure multi-tenancy capabilities. It stands on open-source components including Apache Hadoop, Apache Hive, Apache Solr, Apache Flume, among others.
Built with open source components, Apache HBase is a distributing database built on top of HDFS. It provides random write and read, compression, caching, and also listing capabilities with minimal indexing to create real-time applications like time-series data or social network messages.
When it comes to big data analytics software, Cloudera’s Impala opens up the world of SQL for Hadoop users through its support of ANSI SQL standards where all queries have a schema instead of relying on external table metadata.
Moreover, Apache Spark is an in-memory cluster computing tool that can process iterative algorithms much faster than MapReduce jobs. Because it also works from either the disk or memory. Depending on the size of your dataset Its RDD programming model supports Java, Scala, and also Python.
In-memory computations allow Spark to run up to 100x faster than disk-based computation in Hadoop MapReduce. Because the results are cache in memory explains Peter DeCaprio.
The following are notable features of Apache Spark:
1) Resilient Distributed Datasets (RDDs):
This allows users to store datasets in memory across cluster nodes. Where each partition is a logical subset of the dataset. That can moreover, operate in parallel with common collection functions like map, filter, join, etc.
2) DataFrames:
A data frame is a distributing collection of data organized into named columns which can also query with SQL. It’s built from RDDs but offers richer optimizations under the hood
3) SparkSQL:
This uses Spark’s DataFrame API to run SQL queries on data from multiple sources including Hive, Cassandra, and JSON files.
4) Machine Learning Library (MLlib):
This is a scalable machine learning library written in Java that provides many common algorithms like classification, regression, clustering, etc.
5) GraphX:
This offers a suite of distributed graph processing primitives which allows users to process graphs. With more flexibility than MapReduce-base systems where computation pushes down to the nodes instead of the other way around.
6) Spark Streaming:
This can be use for high-throughput streaming analytics on live data streams from multiple sources. Such as social media sites infrastructure monitoring tools.
Conclusion:
In conclusion, Apache Hadoop is a distributed computing software framework designed. To handle massive amounts of data processing using commodity hardware. Peter DeCaprio says It offers the Map-Reduce programming model. That allows users to process big data from hundreds of nodes from a single user interface.