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- Reduce-based query language with a simple query language.
- Parallelism across a distributed system is provided.
- All cloud platforms have access to a tabular format and interfaces.
- Due to the shuffled data, complex joins may take a long time to complete.
- Execution is dependent on external storage and memory.
- It is easy to store the data that are unstructured
- Easy to retrieve using SQL queries instead of other complicated way
- Large set of data can be stored efficiently
- Apache Hive can provide more flexibility on the Integration.
- Simplify query to devs
- Organize data
- Batch process
- Easy-to-use, interactive modern layout
- Easy to organize data and view tables and views from across the organization
- Fast speed for most queries
- Some queries, particularly complex joins, are still quite slow and can take hours
- Previous jobs and queries are not stored sometimes
- Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
- Sometimes, directories and tables don't load properly which causes confusion
- Please provide some detailed examples of things that Apache Hive does particularly well.
- Migration to the cloud is modern and very secure.
- The best way to do this is to schedule the extraction at times established by hours and quantities.
- So that it can be used normally in daily use, it must be taken into account that the maintenance management of the system so that it works effectively.
- The unification of the data will help to establish the commercial criteria.
- We are sure that the data is protected
- If you try to extract an excessive amount of data, the system will become slow
- You may have the danger that the system collapses due to the amount of data
- It can be used to retrieve data from database like SQL.
- We can partition the data and distribute amongst the clustered machines
- Easily scalable, which gives capability of running analytics at a larger level
- No support for working with Unstructured data.
- ACID properties are not followed like database which creates confusion many times
- Support OLAP environment only, OLTP is not supported
Our use case/scope is to work on a large data analytics project where the data frequency and velocity are very high. Apache Hive is very useful in processing both the unstructured and structured data in a seamless way. It help us in reducing to write complex queries as it is targeted to the SQL queries, we have a engineer team who are very proficient in writing SQL queries with the help of Apache Hive to process the big data.
We have identified no business issues using the solution.
- Apache Hive supports external data tables.
- Supports data partitioning to improve overall performance.
- Apache hive is reliable and scalable solution.
- Apache Hive supports writing ad-hoc queries as well.
- Apache hive is not best suited for OLTP based jobs.
- Sometimes we observed high latency rate while querying data.
- Limitations on providing row-level data update.
- Training materials needs improvements.
The Metastore, is used for storing metadata for each table and its schema. The Driver operates as a controller for executions of the statements. Like other components such as Optimizer and CLI, Thrift Server are some components that enable the processing of big data transformation.
- Used in data warehouse like similar to ETL tools.
- Interface like SQL give data stored in various db group.
- Enables analytics at massive scale.
- Way of framework development can be improved.
- OLTP is not supported.
- Does not offer real time queries.
- Apache Hive is fault-tolerant.
- Apache Hive's latest version supports ACID transactions.
- Apache Hive supports UPDATE, DELETE and MERGE.
- Apache Hive should support ROLLBACK, COMMIT operations.
- Apache Hive should support XML SerDe.
- Apache Hive.
- Simple query language built on top of Ma reduce paradigm.
- Provides parallel execution over distributed system.
- Tabular format and connectors available for all cloud platforms.
- Complex joins may take time to execute due to shuffling of data.
- Static queries mostly.
- Slower than Apache Spark by almost 100 times.
- Dependent on external memory and storage to execute.
It's being used for fetching and generating all the product metrics, for fetching legal data whenever required. All the product history data is stored in it,
It's the one stop cheaper solution for storing and fetching all the analytics data
- It is very easy to set up and start with
- Apache Hive is a cheaper solution for data warehousing and aggregation compared to other products
- One of the cons is the speed which is slightly lesser as compare to other enterprise solutions like BigQuery
- Also, It needs to be maintained by the company itself
If our requirement of aggregation is within seconds for. Terabytes of data then we may have to lookup for other solutions
- Gives access to files stored in a variety of data storage systems
- Facilitates ETL operations, reporting and data analysis
- Supports queries expressed in a declarative language very similar to SQL
- Not suitable for for online transaction processing workloads
- Much more complicated than any typical RDBMS
- Licensing model based on Apache License 2.0
- The SQL-like query language is very familiar to all the CS students. Hence, it's easy to use.
- I used it on a server so I realize it is very scalable and can be used to process small and big datasets.
- I particularly liked the UDF functionality where the user could define functions to produce particular output.
- Transactions are not supported
- Lack of subqueries made some tasks achievable only when completing one query and then the subsequent one
- It is not as fast as spark.
On the other hand, it's definitely slower than some other alternatives such as spark. Also, it's not recommended to use it in processing small datasets. Pandas and other normal data loading libraries can be useful to deal with small datasets.
- Flexibility through schema on read
- Familiar SQL like query language
- Functions for complex queries and analysis
- Slower processing than other tools on the market
- The SQL, like query interface, is the core value and shining core of the Hive.
- It supports various data formats stored and also allows indexing.
- It is fast.
- No transaction support.
- No sub-query support.
- Can only deal with the cold data (non-real time).
It was one of those technical sessions and I was supposed to demonstrate a word count program of a novel downloaded from the Project Gutenberg. I was successfully able to download the novel, load it into the Hadoop platform and execute a HiveQL (a SQL similar syntax used by Apache Hive) query to demonstrate for few unique words, their count, and related examples.
- The capability to handle large amounts of data and its querying process.
- A syntax similar to SQL is an added advantage.
- An active developer support and community always ready to help.
- Ease of usage.
- Resource consuming sometimes. May be that I was using a larger object file.
- Needs to add an update or a modify functionality. This has to be the minimilastic CRUD requirement.
The only underlying problem could be that the Apache Hive is designed to run on the Apache Hadoop ecosystem. People who are not comfortable using a Linux tree structure based File System or even people who are not likely to use a Linux OS might not like to use Hive.
- Reading databases
- Writing databases
- Storing databases
- Distributed databases
- Improvement techniques for handling Relational Data
- Advanced optimizations
- Transactions memory
- Monitor query performance
- Manage tables in the data warehouse
- Uses standard SQL
- UI is quite dated and not intuitive
- Open-source, so does not have consistent updates or support
- Not the most optimal for ETL processes
- Querying in Apache Hive is very simple because it is very similar to SQL.
- Hive produces good ad hoc queries required for data analysis.
- Another advantage of Hive is that it is scalable.
- Apache Hive isn't designed for and doesn't support online processing of data.
- Sub queries not supported.
- Updating the data can be a problematic task.
- It's Fast!
- You can store a different kind of data structures here other than the standard ones
- Good scalability
- Good redundancy too
- It's not as ACID compliant as an RDBMS. It's a recently added feature and still needs work.
- This is not the tool to go for online data processing.
- It does not support sub-queries.
- It can't process data in real time.
Its good for fast query processing, for storing large amounts of data.
- Querying, joining and aggregating data
- In built-in and user-defined functions
- Support for other big data frameworks like Spark
- Need better user interfaces for browsing datastores and querying
- One of the standard SQL on Hadoop implementations. Comes installed in both HDP and CDH Hadoop distributions.
- Hive Live Long and Process has made recent significant improvement on long-running queries.
- Allows BI tools to run analysis over Hadoop data.
- Allows various relational databases for its metastore. These include MySQL, Postgres, Derby, or Oracle.
- Needs to keep up with execution engine improvements. Spark or Tez on Hive, then LLAP are good starts.
- Overall speed of ad-hoc querying could be improved.
- Can query on large sets of data and fast when compared to RDBMS
- Can use SQL for data access and no need to learn new language
- Can write custom functions (UDF) with python and also Java
- Security roles for different users should be implemented
- All the functionalities of SQL should be available
- To query on large sets of data
- Faster access compared to traditional Databases
- OLAP projects
- Data Warehousing project
- To get insights from GigaByte's or TeraByte's of data
- Rule based projects and also to identify the patterns in data
- For applying transformations on large sets of data
- Faster response time than traditional databases
- Also able to get connected with hadoop components
- For complex analytical and different types of data formats