SQL, Spark And Hadoop Training

SQL is a query language used for retrieving data from relational database

SQL can be executed over a single table or set of tables.

SQL is mostly used for small data

SQL is not used for processing large datasets

SQL is not used for data analysis.

SQL is not parallel processing.

SQL is not used for data warehousing.

Sparkis a high-performance cluster computing platform that enables fast processing of large datasets and complex analysis.

Spark can be used to process both small and large datasets.

Spark is parallel processing and can be used for data warehousing. 

Hadoopis a big data platform that enables users to easily store, process and analyze large amounts of data.

Hadoop can be used for data warehousing and large-scale parallel processing.

Hadoop is not as fast as Spark or SQL, but it’s more versatile.

The applications of BigData are vast and ever-growing, with potential for huge benefits for businesses of all sizes. By using SQL, Spark and Hadoop together, you can unlock the potential of your data and turn it into powerful insights that can drive innovation and growth.  

1. SQL is used to store,modify and retrieve data which is stored in a relational database. SQL is used in databases to store and retrieve data. SQL is used in web development to query databases and generate dynamic web pages. SQL is used in mobile app development to query databases and generate dynamic mobile apps.

2. Spark is used for processing and analyzing data stored in HDFS. Spark is used in Big Data analytics to process and analyze large data sets. Spark is used in machine learning to train and predict algorithms. Spark is used in streaming data analysis to analyze real-time data streams.

3. Hadoop is used for storing and processing large data sets. It is also used for running Map Reduce jobs. Hadoop is used in distributed computing to process and store large datasets. Hadoop is used in Map Reduce to process large data sets.

4. All three can be used together to get the most out of big data. For example, data can be stored in HDFS using Hadoop, then analyzed using Spark, and finally stored back in a relational database. This way, all the data is kept in one place, making it easier to analyze. Further more, by using different tools for different tasks,you can get more out of your data. For example, you can use Map Reduce to quickly process large data sets, while SQL allows you to more easily store and manipulate the data. Finally, by working together these tools help speed up the entire big data processing workflow.