In today’s Era Data is growing at a very fast pace. Wide Spectrum of tools/technologies such as HDFS, Sqoop, Hive, Impala, HBase, Spark, Kafka, Nifi, and many more are available to conquer this ever growing mountain of data. Above all this there is a basic need of understanding and mastering Big Data.

Let’s explore the Logical Architecture of Big Data:

The very first layer is of Hardware, which can be on-premise or Cloud(AWS, Azure, GCP, etc.). After that there is a layer of Operating System (Debian, Linux, RedHat, etc.), on which you can install and build your solution. Then we have Java specifically JDK(Java Development Kit) as another pre-requisite for installing key Big Data Ecosystem projects like Hadoop, Kafka, Spark etc. After that we have a layer of Distributed Storage which includes HDFS(Hadoop Distributed File System), HBase, Kudu, and many more. Then we have a various set of tools which would help us to counter Big Data. First of all we will cover all the tools that require YARN as a Resource Manager. So over the YARN layer we have a layer of Distributed processing. It includes MapReduce(old now), Tez, Spark. There can be other distributed processing tools but these 3 are majorly used tools. For Data Ingestion, we have Sqoop, Flume, Kafka etc. For Analysis, we have Hive LLAP, Impala, Presto and Hive. In Spark, there are a set of tools available such as R for analysis, SparkML for using ML, Spark SQL for using SQL queries, Graph for analysis using graph, SparkStreaming for real time data. Other than this we have Impala for Data Analysis, Kafka is an event stream platform, Nifi for managing DataFlows, Elastic search for Data Exploration, HUE is Hadoop User Experience, Oozie/Airflow is used for workflow creation/scheduling. These tools don’t require YARN, they have their own daemons.

Isn’t it puzzling to select few among the set of many? Especially when there is a need to develop complete end-to-end pipelines. Because, your selection will decide how efficiently you will conquer big data.

If you are also confused among these tools, then “Big Data for Architects” is the best solution to this problem.

After this course you would be able to choose specific tools from a bunch of tools available. In course comparison between various tools which are required for each stage of the Big Data pipeline. Along with hands-on experience on majorly used tools in the market. It provides the enough knowledge required to handle Big Data easily.

You can checkout our another course which is the recommended pre-requisite for above course “Big Data Crash Course”.