What Should We Know about Big Data Technologies
The need for big data tools and technologies is increasing nowadays as traditional databases no longer function adequately when dealing with massive volumes of information. Fortunately, technological progress offers new alternatives to traditional tools, allowing companies to store and process unstructured and semi-structured data. Many of big data technologies are extensively used all over the world. Here is a list of top ten big data tools and technologies:
- Apache Beam
Modern companies are getting serious about finding big data business intelligence technologies that meet all their requirements. Among the technologies, Hadoop and Spark are given increasing priority. These two platforms provide basic infrastructure to analyze big data. James Kobielus specifies that 4% of companies rely on Hadoop while 18% mention they use this technology on a limited basis. 20% of companies plan to use Hadoop in the coming year. As for Spark, Ryan Spain states that 13% of companies currently use Spark, 22% evaluate it, and 20% say they plan to use it in the nearest future.
Hadoop and Its Ecosystem
What Is Hadoop?
Hadoop is a software framework used to analyze huge data sets. It gained its increasing popularity due to an ability to access, store, and analyze huge amounts of data both cost effectively and quickly through computer clusters of commodity hardware. Hadoop is complemented by a variety of projects aimed at providing specialized services. Collectively called Hadoop Ecosystem, these projects make Hadoop more usable and widely accessible. Ecosystem components of various types and complexity assist its users in solving business problems. They all are divided into four layers: data processing, data management, data storage, and data access. Sneha Mehta and Viral Mehta state that the holistic view of this technology gives prominence to Hadoop YARN, Hadoop MapReduce, Hadoop Distributed File System, and Hadoop common.
Hadoop YARN is a framework for cluster resource management and scheduling. Hadoop common provides OS-level abstractions, utilities, Java files, libraries, and script necessary for Hadoop functioning. HDFS (Hadoop Distributed File System) provides access to application data. Hadoop MapReduce aims at processing huge data sets. Hadoop ecosystem also includes other projects. Data storage layer comprises not only HDFS but also HBase. HBase is a distributed scalable database which backs structured data storage for big tables.
Data access layer consists of Hive, Pig, Mahout, Avro, and Sqoop. Hive functions as data warehousing, providing ad hoc querying and summarization. Mahout serves as a library for data mining and scalable machine learning. Pig is an execution framework and dataflow language for parallel computation whereas Avro is a system of data serialization. Sqoop belongs to connectivity tools that move data from non-Hadoop stores, like data warehouses or relational databases, into Hadoop. Data management layer ensures user`s access to the system. It consists of four systems, each of which enables rapid and easy data processing. These are Oozie, Apache Chukwa, Apache Flume, and Apache Zookeeper. Each of Hadoop layers and systems contributes to the effective and proper functioning of the framework.
Spark and Its Ecosystem
Spark is a cluster-computing open-source framework which is also considered one of the most effective big data technologies. Matthew Mayo specifies that its ecosystem consists of six major components and many supplementary projects. These are Spark DataFrames, Spark SQL, Spark Streaming, MLlib, GraphX, and Spark Core API. Spark DataFrames is a distributed collection of information sorted into named columns. Spark SQL is a technology that executes SQL queries and reads data from Hive installation. Spark Streaming enables high-throughput live data processing. MLib is a machine learning library which comprises common utilities and learning algorithms. GraphX is a relatively new constituent of Spark. It is used for graph-parallel computing. Finally, Spark Core API generates APIs for commonly used languages, including SQL, Java, R, Python, and Scala.
Where Spark and Hadoop Are Used and What Role They Play in Ukrainian Business
Both Spark and Hadoop are big data analytics technologies which are widely used in business. While Hadoop enables reliable and secure data storing, Spark ensures careful processing of huge data sets. Hadoop and Spark are implemented in med
ia, IT consulting and development, advertisement, marketing, retail industry, banking and finance, manufacturing, and even health care. In Ukraine, like in many European countries, Hadoop and Shark are widely used to process data and reveal the main tendencies. Modern Ukrainian companies started relying on these platforms to collect data about consumer behavio
r and get ready for potential market shifts. Retail industry is the main sector where these platforms are actively implemented. We expect that the percentage of entrepreneurs taking advantage of big data technologies will increase in the nearest future.
Evidently, big data management technologies and applications ensure high-quality analysis of big data. They enable product research and scoring, network optimization, fraud prevention, successful advertising campaigns, management, and other useful options that boost companies` success and increase their revenues.