“The world we live is one big data problem.” – by Andrew McAfee
Companies all over the world are generating an enormous amount of data. The Data collected in the whole of the 20th century is equal to data of just two days. Hence, when we open our LinkedIn profiles, we come across companies looking to hire professionals with Big Data Hadoop Professionals or providing Big Data Hadoop to upscale their workforce.
Let us look at the features and underlying architecture of Big Data.
Learning Of Blog
- What is Big Data?
- Characteristics of Big Data
- Big Data Hadoop
- The architecture of a Hadoop
- Apache Hive
- Apache Hive Architecture
What is Big Data?
Big Data is the term used for those sets of data whose size is large, diverse and could include unstructured data or structured data sets. The data generated is generally real-time and could have a different source of origin. Hence, Big Data analytics is the field that deals with different ways to understand and gather information or insights from the data.
Characteristics of Big Data
Increasing and diverse set of data now available, many researchers and companies define the data based on 17 V’s and 1C (volume, velocity, value, variety, veracity, validity, visualization, virality, viscosity, variability, volatility, venue, vocabulary, vagueness, verbosity, voluntariness, and versatility, and complexity). But most of the basics are covered by the 3 V’s Volume, Velocity, and Variety.
- Volume: It is the amount of data generated and stored. The data ranges from terabytes to zettabytes. The data coming from smart devices, IoT enabled devices, machines, social media.
- Velocity: The data needs to be capture near real-time with speeds at which information coming from your youtube video, sensors, RFID, tags, etc. with speeds of gigabytes per second.
- Variety: Information coming from diverse sources mobile, emails, videos, audios that can be structured or unstructured.
Hence companies are looking for keywords in CVs which have Big Data Certification in them.
Advantages of Big Data
Companies looking to future proof their market or to capture new ones look at the data for the opportunity. Below are a few of the advantages companies gain over their competitors with Big Data.
- Machine learning and Artificial Intelligence requires Big Data to train them to help firms make better decisions.
- Forecasting and predictive models need significant data to be able to provide accurate results.
- Customer demands, marketing, and product development require data as to what will be the response of the product, how well has the marketing campaign response been, and anticipate future demands.
Big Data Hadoop
Big Data Hadoop certification is one of the most sought after courses amongst the big data certification courses. Ever wondered what Hadoop is and how it helps in Big Data. All the different types of data are in a data lake, which is a storage repository that stores an enormous amount of unstructured data in its original format until it is needed. Hence, the Data lake may require multiple servers that can operate and process in clusters, often based on technologies like Hadoop and Spark.
Therefore, Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Its purpose is to move from single servers to thousands of servers, each offering individual computation and storage. It can detect and handle multiple failures at the application layer, so providing a service on top of a cluster of machines, each of which may be prone to failures.
The Architecture of a Hadoop
Hadoop Base/Common: Hadoop common provides utilities like libraries that are required by the other Hadoop components to perform.
HDFS (Hadoop Distributed File System): It takes care of how the data is storing in the Hadoop Clusters. It based on Master/Slave Architecture and maintained and managed the data.
Master Node/Name Node: Name node maintains and operates the data stored in HDFS. HDFS can have only one Master Node, and if that stops working, there would be a backup node called the secondary name node.
Slave Node/Data Node: Data nodes store the actual data in blocks.
Replication: Hadoop framework stores its data by dividing it into chunks, which is to store in three different data nodes as replication data. Hence if one of the data nodes goes down, then the data can be retrieved by the other replicated data node. The accessibility of data is improved, and the loss of information is to avoid.
YARN: It takes care of job scheduling and resource management.
MR (Map Reduce): It is to process/query the data within the Hadoop framework.
We have talked about how data is stored, but next, we need to understand how information is retrieved and analyzed. Professionals with Big data analytics certification with expertise in the same are sought after by companies. Hence knowledge of Apache Hive is essential. It is an application for data warehouse software, facilitating reading, writing, and managing large datasets residing in distributed storage using SQL. The structure goes onto the data already in the room. Therefore, it runs over or on top of the Hadoop framework. It is one of the critical components of how we analyze our data and get insights. The structure used is HQL (Hive Query Language), which provides features like Indexing, Built-in user-defined functions (UDFs), and functions like manipulating dates, strings, and other data-mining tools.
Apache Hive Architecture
The underlying architecture of Apache Hive
Hive Clients: It supports programming languages like SQL, Java, C, Python using drivers such as ODBC, JDBC, and Thrift.
Hive Services: The execution of commands and queries takes place at hive services. It consists of five sub-components.
- CLI: Default command-line interface provides the implementation of Hive queries/commands.
- Hive Web Interfaces: It is a graphical UI that is an alternative to the Hive command line and used to run the queries or commands in Hive application.
- Hive Server/ Apache Thrift: It is responsible for taking controls from different command-line interfaces and submitting the commands and queries to Hive; also, it retrieves the results.
- Apache Hive Driver: It takes inputs from Hive services by a client and transfers the information to metastore.
- Metastore: Hive’s metadata stores information such as the structure of tables, partitions & column type, etc.
Hive Storage: It is the location where the actual task gets performed. All the queries that run from Hive acted as Hive storage.
Thus, to conclude, any student, developers, and professionals who are into analytics and require to upscale their domain knowledge or to make a career in Big Data Analytics should sign up for Big Data Certification using the Hadoop Framework as one of the go-to course.