Apache Hive is a highly scalable, distributed, fault-tolerant data warehousing tool that sits on top of Apache Hadoop. It provides users with HiveQL which is identical to SQL and is compatible with the Amazon S3 file system.
It is used in combination with MapReduce which saves intermediate results on disks and allows batch-style data processing, making it fault-proof/tolerant.
According to Datanyze, Hive has a market share of 10.47% and the top industries that use it are:
It is used by industry leaders like Facebook, Netflix, Atlassian, Alibaba, Uber, and Airbnb.
Presto, on the other hand, was developed by Facebook as an in-memory distributed SQL query engine for Big Data to directly handle interactive queries in Hadoop/HDFS against several internal data stores, including its 300 PB data warehouse at the time.
Facebook made Presto open source in November 2013 and it is compatible with query data sources like:
It enables users to access many data sources inside a query without requiring MapReduce to operate on constituent data in HDFS like Apache Hive since it has its proprietary mechanism for breaking down and distributing the process for a particular query.
Presto doesn’t have its own storage system and thus, it requires organizations to use it in combination with Hadoop. Presto, which is utilized by Apple, Walmart, and Amazon and has a market share of 9.04%, powers 5,876 websites in sectors such as:
In this article, we will compare them on multiple parameters but it is noteworthy that the success depends on accurate need analysis, planning and implementation, and significant investment in the form of financial resources and technical talent. Having said this, every organization might not be willing to risk time or money for building a functional tool stack and technical teams iteratively.
That’s where xAqua, a new breed unified data platform fits in; it provides an end-to-end solution for all data needs which virtually eliminates the requirement for an expansive tools and solutions stack, thereby reducing costs and dependency on technical staff.
After reading the article, you will be able to choose between Hive, Presto, and xAqua for your data requirements.
From a speed perspective, Presto is the faster solution when compared to Hive due to its distributed scale-out architecture and massively parallel processing (MPP) design. Presto has a resource manager and scheduler which improves cache read, making it faster than most relational databases.
Also, the fact that it has in-memory pipelined execution adds to its ability to compute faster. The ability of Presto to read unmodeled, unprocessed data straight from S3 is another technological aspect that contributes to this regard. This means you do not have to wait for ETL to process the data before you can analyze it.
From a scalability perspective, Presto is the better choice when concurrent queries are considered due to its MPP database architecture and in-memory capabilities as they facilitate horizontal scalability in processing enormous amounts of data. Presto can function at massive scales to run interactive and/or ad-hoc SQL queries with sub-second performance.
For analyzing large amounts of data and reporting, Hive is a better option since it doesn’t have any limitations to the volume of data you can process, at least in the normal scenario from an enterprise point of view. Especially, eCommerce companies should prefer Hive since it allows eCommerce companies to use custom code while Presto doesn’t. Presto can handle very limited eCommerce data when compared to Hive, which makes the latter’s reporting capabilities far better.
From a learning curve perspective, Presto is relatively better since it uses the standard ANSI SQL language which most developers are familiar with. Hive, on the other hand, uses HiveQL which is slightly different from SQL due to a few of its oddities. Although it has provided a detailed resource for developers to learn HiveQL, it doesn’t take significant time to learn it as long as the developer is acquainted with the standard SQL. Thus, when considering solely the query language, it is easier to use Presto for all functions like modifying the database, retrieving data, and executing queries.
From an Analytics point of view, the choice between Hive and Presto will depend on your specific use case and the types of data you are working with. If you are primarily working with structured data and need to perform batch processing, Hive may be a better choice. However, if you need to perform ad-hoc queries and interactive analysis across multiple data sources, Presto may be a better fit.
This is because Apache Hive is a Hadoop-based data warehousing system that is ideal for structured data organizations and excellent for batch processing massive datasets and storing the results. Presto can retrieve data from Hadoop, NoSQL databases, and cloud storage systems while being deployed on the cloud, on-premises, or even on a hybrid basis. Thus, it is a fantastic option for enterprises that need to rapidly explore and understand data due to its support for ad-hoc searches and interactive analysis.
Presto makes it easy to make a self-service BI layer that more than just data engineers can use, and it gives data consumers faster access to data. It uses techniques like parallel processing in memory, chained execution across nodes in a cluster, a multi-threaded execution model that keeps all CPU cores busy, and efficient flat memory data structures to handle distributed query processing. Hence, in terms of accessibility of analytics, Presto is better.
In this section, we will go through the pros and cons of Hive and Presto which we didn’t discuss above:
Hadoop Integration: Since Hive is built on top of Hadoop, it is designed to work well with other Hadoop parts like HDFS, YARN, MapReduce, and its ecosystem tools like Pig, Sqoop, and Spark. This makes it simpler for developers to move data between tools and do complex tasks that involve processing data. It also lets developers connect to external data sources, like relational databases and object stores, so they can get data from more than one place.
Hive is also compatible with popular BI tools like Tableau, Microsoft Power BI, and QlikView, so users can easily create reports and visualizations from the data stored in it. Thus, Hive supports integration for a wide range of tools making it the most ubiquitous tool for Hadoop engineers.
Hive is very flexible and scalable: Hive can manage massive volumes of data and supports a variety of file formats such as CSV, JSON, ORC, and Parquet. This allows developers to deal with a wide range of data sources and types without worrying about data format conversions or data loss.
Hive’s SQL-like interface also offers a high-level abstraction layer over Hadoop, allowing developers to focus on working on the logic of their queries instead of the underlying data storage and retrieval issues. This makes complicated queries simpler to construct and optimize for speed from the operator’s point of view.
Slower Performance: Apache Hive’s performance can be termed as slower than traditional databases because of its nature of running queries on large datasets in a distributed environment.
Queries executed in Hive are processed by distributed computing systems like Hadoop, which delegate the work across multiple nodes in a cluster. This results in increased latency and overhead, which can slow down query execution times compared to traditional databases. Its Hive’s SQL-like interface is not well-suited for real-time transactional processing which impacts its performance, especially when dealing with complex queries or large datasets. Hence, Hive can be slow when considering the user’s expectations.
Limited Data Types: As mentioned earlier, Hive is primarily designed to work in combination with Hadoop which typically supports limited data types. Hive is aimed at batch processing and data warehousing and not at real-time transactional processing, so it doesn’t need to support the same range of data types as traditional databases. In fact, updating/deleting records in Hive can be tedious and exhausting even with Hive ORC and transaction support.
Interestingly, file formats, such as CSV and ORC, have a limited set of data types that they support, which in turn limits the data types that Hive can work with. Hence, Hive will not be the most suitable candidate for use cases that require working with certain types of data, such as complex structures or unstructured data.
Setup complexity: Setting up and configuring Hive involves working with a number of different components within the Hadoop ecosystem, including HDFS, YARN, and Hive’s own metadata store. One needs to be familiar with concepts such as data partitioning, data replication, parallel processing, and troubleshooting issues arising in distributed environments.
It needs to be configured to work effectively across multiple nodes in a cluster. This involves setting up data partitions, configuring data replication, and optimizing query performance, among other tasks.
Handles complex queries at large scale: Its distributed architecture processes queries across multiple worker nodes which facilitates parallel processing which is further supported by in-memory processing.
Also, it uses cost-based query optimization techniques to choose the most efficient query plan for a given query. Combining its dynamic data partitioning and columnar processing, Presto proves to be excellent at handling complex queries on a massive scale.
Better data compatibility: Presto can access and process data stored in Hadoop Distributed File System (HDFS) as well as data stored in other Hadoop-compatible file systems such as Amazon S3 and Azure Data Lake Storage. It can also connect with NoSQL databases like Cassandra and relational databases like MySQL, PostgreSQL, Oracle, and SQL Server.
It also connects to sources such as Kafka, MongoDB, and Redis, thereby providing excellent data compatibility. Presto has more built-in UDFs than Hive, so users can work with complicated data structures without having to write their own UDFs. Developers can use JDBC and ODBC drivers in Presto and connect them with Hive to increase its speed.
Flexible data modeling: Presto works with both structured and unstructured data which can help organizations gain insights from multiple sources including social media. CRM, and sensor data. Developers can use SQL queries to source this data which saves a significant portion of developer time.
SQL-like Interface: Developers with existing knowledge of SQL can expect a fairly shorter learning curve to interact with the large-scale datasets stored in Hadoop. Also, Presto is an open-source tool with a large and active community of users and contributors.
Combining the large contributor base with its SQL-like interface, developers can benefit from the extensive documentation, tutorials, and support available online for quickly troubleshooting any issues encountered.
Presto has a limited tool ecosystem: One of the downsides that Presto users can expect to suffer is its limited tool ecosystem which can be concerning. This limits its ability to serve organizations in some use cases which aren’t mainstream. However, with big players like Facebook and Netflix using it, users can expect the situation to improve in the days to come.
High Costs: To get the required level of performance, organizations may need to spend dearly on hardware and infrastructure since Presto is designed to work with large amounts of memory, processing power, and storage to perform well. For instance, configuring Presto needs users to reserve a substantial amount of RAM on nodes already running YARN.
Its distributed environment translates to multiple nodes working concurrently to process data which also adds to the increased costs incurred due to the required high-performance servers, storage systems, and networking equipment.
It is noteworthy that both Hive and Presto share a few similarities as highly secure, open-source MPP SQL engines, they aren’t comparable- they are complementary to each other. Presto is mostly used for analytical querying, while Hive is primarily used for data access.
(Un)surprisingly, the overall success rate for major big data, AI-ML, and analytics projects sits at a mere 15%. 85% failure rate testifies to the fact that despite spending millions on modern data warehousing technologies- the main issues with Big Data project implementation weren’t addressed. People, processes, and data quality issues have been the major underlying problems.
Another factor that stops organizations from solving basic issues is the financial entry barrier for such data-centric technologies. For instance, in order to configure a Hive instance, one needs to understand 113 pages of technical documentation. If that isn’t enough, companies that build data products themselves lose track of what the tool is supposed to do.
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When compared to the DIY tool stack, you can expect better scalability, improved data quality, and lower costs since you don’t need to run and maintain an extensive suite of tools and technologies.
The choice between Hive and Presto depends on your specific use cases and chances are, you might need both of them (and a lot more tools as well) which can be costly and inefficient. A unified data platform like xAqua can be a better alternative but again, it depends on your requirements, expectations, and other factors.
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