Ad
More Related Content
Similar to New big data architecture in hadoop.pptx (20)
Recently uploaded (20)
Ad
New big data architecture in hadoop.pptx
- 1. New Big Data Architecture • A big data architecture is designed to handle the ingestion, • processing, • and analysis of data that is too large or complex for traditional database systems.
- 2. Involves types of workload: • Batch processing of big data sources at rest. • Real-time processing of big data in motion. • Interactive exploration of big data technologies and tools. • Predictive analytics and machine learning.
- 4. New Big data architecture components: 1.Data sources: All big data solutions start with one or more data sources. Examples include: • Application data stores, such as relational databases. • Static files produced by applications, such as web server log files. • Real-time data sources, such as IoT devices.
- 5. 2.Data storage: • Data for batch processing operations is typically stored in a distributed file store that can hold high volumes of large files in various formats. • This kind of store is often called a data lake. • Options for implementing this storage include Azure Data Lake Store or blob containers in Azure Storage.
- 6. 3.Batch processing: • Because the data sets are so large, often a big data solution must process data files using long-running batch jobs to filter, aggregate, and otherwise prepare the data for analysis. • Usually these jobs involve reading source files, processing them, and writing the output to new files. • Options include running U-SQL jobs in Azure Data Lake Analytics, using Hive, Pig, or custom Map/Reduce jobs in an HDInsight Hadoop cluster, or using Java, Scala, or Python programs in an HDInsight Spark cluster.
- 7. 4.Real-time message ingestion: • If the solution includes real-time sources, the architecture must include a way to capture and store real-time messages for stream processing. • This might be a simple data store, where incoming messages are dropped into a folder for processing. • However, many solutions need a message ingestion store to act as a buffer for messages, and to support scale-out processing, reliable delivery, and other message queuing semantics.
- 8. 5.Stream processing: • After capturing real-time messages, the solution must process them by filtering, aggregating, and otherwise preparing the data for analysis. • The processed stream data is then written to an output sink. Azure Stream Analytics provides a managed stream processing service based on perpetually running SQL queries that operate on unbounded streams. • You can also use open source Apache streaming technologies like Spark Streaming in an HDInsight cluster.
- 9. 6. Analytical data store: • Many big data solutions prepare data for analysis and then serve the processed data in a structured format that can be queried using analytical tools. 7.Analysis and reporting: • The goal of most big data solutions is to provide insights into the data through analysis and reporting. • To empower users to analyze the data, the architecture may include a data modeling layer, such as a multidimensional OLAP cube or tabular data model in Azure Analysis Services. • Analysis and reporting can also take the form of interactive data exploration by data scientists or data analysts.
- 10. 8.Orchestration: • Most big data solutions consist of repeated data processing operations, encapsulated in workflows, that transform source data, move data between multiple sources and sinks, load the processed data into an analytical data store, or push the results straight to a report or dashboard. • To automate these workflows, you can use an orchestration technology such Azure Data Factory or Apache Oozie and Sqoop.
- 11. Apache HIVE • The Hadoop ecosystem contains different sub-projects (tools) such as Sqoop, Pig, and Hive that are used to help Hadoop modules. • Sqoop: It is used to import and export data to and from between HDFS and RDBMS. • Pig: It is a procedural language platform used to develop a script for MapReduce operations. • Hive: It is a platform used to develop SQL type scripts to do MapReduce operations.
- 12. What is HIVE • Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy. • Initially Hive was developed by Facebook, later the Apache Software Foundation took it up and developed it further as an open source under the name Apache Hive. It is used by different companies.
- 13. Hive is not • A relational database • A design for OnLine Transaction Processing (OLTP) • A language for real-time queries and row-level updates Features of Hive • It stores schema in a database and processed data into HDFS. • It is designed for OLAP. • It provides SQL type language for querying called HiveQL or HQL. • It is familiar, fast, scalable, and extensible.