Note that for high availability, you can configure a backup of the metadata. Apache Hive is integrated with Hadoop security, which uses Kerberos for a mutual authentication between client and server. Permissions for newly created files in Apache Hive are dictated by the HDFS, which enables you to authorize by user, group, and others. Apache Hive is ideal for running end-of-day reports, reviewing daily transactions, making ad-hoc queries, and performing data analysis.
Such deep insights made available by Apache Hive render significant competitive advantages and make it easier for you to react to market demands. Apache Hive and Apache Pig are key components of the Hadoop ecosystem, and are sometimes confused because they serve similar purposes. Both support dynamic join, order, and sort operations using a language that is SQL-like. Pig is mainly used for programming and is used most often by researchers and programmers, while Apache Hive is used more for creating reports and is used most often by data analysts.
The following table identifies further differences to help you determine the best solution for you. HiveQL is the language used by Apache Hive after you have defined the structure. Additionally, HiveQL supports extensions that are not in SQL, including create table as select and multi-table inserts. Following is a list of a few of the basic tasks that HiveQL can easily do:. Apache Hive integration is imperative for any big-data operation that requires summarization, analysis, and ad-hoc querying of massive datasets distributed across a cluster. It provides an easy-to-learn, highly scalable, and fault-tolerant way to move and convert data between Hadoop and any major file format, database, or package enterprise application.
Watch Getting Started with Data Integration now. Watch Now. With big data integrated and easily accessible, your business is primed for tackling new and innovative ways of learning the needs of potential customers. You can also run your internal operations faster with less expense.
Shop Programming Hive. Data Warehouse And Query Language For Hadoop
To truly gain business value from Apache Hive, it must be integrated into your broader data flows and data management strategy. The open-source Talend Open Studio for Big Data platform is ideal for seamless integration, delivering more comprehensive connectivity than any other data integration solution. This means you can move and convert data between Hadoop and any major file format, database, or package enterprise application. As the first purely open-source big data management solution , Talend Open Studio for Big Data helps you develop faster, with less ramp-up time.
6 SQL Data Warehouse Solutions For Big Data
Using an Eclipse-based IDE, you can design and build big data integration jobs in hours, rather than days or weeks. You can change your ad preferences anytime. Upcoming SlideShare. Like this document?
Hive and IBM Db2 Big SQL
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Embeds 0 No embeds. No notes for slide. Synopsis book Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoops data warehouse infrastructure. This example-driven guide shows you how to set up and configure Hive in your environment, provides a detailed overview of Hadoop and MapReduce, and demonstrates how Hive works within the Hadoop ecosystem.
Youll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data.