Are you a developer,tester,manager working any any technology but want to move to Big Data and you want to learn more about Apache Hadoop technology?
Are you looking for a practical step by step guide to get you started with Hadoop Development ?
Do you want to start working on Hadoop within a week time?
If all the above related to you then you have landed to the right course
This is a hands on based course which includes demo on all the major topics in Hadoop and Spark.
Data is a profitable asset that helps organizations to understand their customers better and therefore improve performance.Hadoop is an open-source software framework to solve problems involving massive amounts of data and applications and help in running those on clusters of commodity hardware. Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis.Hive is not designed for OLTP workloads and does not offer real-time queries or row-level updates. Hive looks very much like a traditional database code with SQL access. However, because Hive is based on Hadoop and MapReduce operation, there are several key differences
Hive is considered as one of the popular data warehouse application to store and summarize structured Big data sets by facilitating us to write SQL like queries to efficiently extract business data. It converts SQL queries into Map reduce(MR) programs and process it distributive, hive uses MR as a default execution engine and it can process data available in HDFS, S3 any distributed file system per say.
When we talk about Apache Hbase we refer the nosql as part of hadoop ecosystem . HBase is a column oriented data store which uses HDFS as an underlying storage. Data storage unit in HBase is column i.e. columns are stored sequentially in contrast to RDBMS where data storage unit is row and rows are stored sequentially.
Spark is one of the top job destination of the world . This course is aiming at addressing the common daily tasks the developer are getting and how to resolve the tasks in effective way . This course is also introducing Big Data to understand the basic concept of the problem because of which we are learning different tools and technologies to address the problem. Scala is considered as better java as we can include all the libraries used in Java in Scala and therefore Scala becomes a very strong language .If you're already working on a Java written codebase, part of the team can start working with Scala and still uses the Java classes already written. Scala's syntax is less of a burden than Java's. You don't have to write your trivial getters/setters each time. You can use special method names (à la C++ operators overloading) when it makes your code cleaner. Also, the pattern matching is incredible, you will want to use it everywhere.
Looks odd but most language features which are present in Scala are not unique and could be related to other languages. What makes Scala successful is the way these features are combined.
Most people start using Scala as a better version of whatever mainstream language they were previously using (Java, C#, Ruby, Python, etc). And indeed, it’s not difficult to show that Scala has serious advantages over them. For example, it’s much more concise than Java or C#, more performant and type-safe than Ruby and Python etc. Given the right approach, it’s quite easy to pick it up .
So how does Spark differs from Scala ? Is there any difference or both are same thing . Apache Spark is an open source framework for running large-scale data analytics applications across clustered computers. It can handle both batch and real-time analytics and data processing workloads. On the other hand, Scala is a general-purpose programming language that supports functional and object-oriented programming. It is compiled and run on Java Virtual Machine (JVM). Scala improves productivity, application scalability and reliability. In brief, Scala is considered as the primary language to interact with the Spark Core engine.