Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
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Presto
Score2.6 out of 10
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Presto is an open source SQL query engine designed to run queries on data stored in Hadoop or in traditional databases.
Teradata supported development of Presto followed the acquisition of Hadapt and Revelytix.
Software Engineer in Engineering at LinkedIn (Internet, 5001-10,000 employees)
Chose Apache Spark
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Engineering Manager - Ride Experience in Engineering at Uber (Internet, 5001-10,000 employees)
Chose Presto
I think Presto is one of the best solutions out there today at the cutting edge for interactive query analysis. One of the challenges is presto is a niche tool for the interactive query use case and doesn't have the knobs and whistles as much as Spark. In the foreseeable future …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Apache Spark and Presto are open-source distributed data processing engines. Both engines are designed for ‘big data’ applications, designed to help analysts and data engineers query large amounts of data quickly. Although they have many similarities, Presto is focused on SQL query jobs, while Apache Spark is designed to handle applications that require more computational analysis, such as machine learning.
Both Apache Spark and Presto are used mostly by large enterprises, with a significant mid-sized company user base as well. Since both engines are designed for big data processing, they’re often overkill for smaller businesses.
Features
Although both Apache Spark and Presto are used for similar applications, they each have distinguishing features that set them apart from each other.
Apache Spark is designed for fast data processing in a variety of contexts, including machine learning, ETL, and ad-hoc querying. It uses an in-memory processing design, meaning it can run with very few disk read/write operations and process enormous datasets quickly. Developers report that its SQL interface and object-oriented design make it easy to understand and write code for. Users also appreciate its wide variety of APIs for ETL procedures and cluster management. Apache Spark has a large support community and wide industry adoption, and the internet has plenty of recommended solutions to common problems.
Presto is optimized specifically for SQL, meaning it can exceed Apache Spark’s speed for SQL queries. It queries data in-place, without copying or moving data. Presto also uses a flexible, plug-and-play architecture that makes it easy to combine and simultaneously query data from multiple sources, including both SQL and NoSQL databases. It’s suitable for ad-hoc querying, batch ETL jobs, and data analysis for A/B testing.
Limitations
Before adopting Apache Spark or Presto, consider the limitations of each engine.
Apache Spark’s in-memory processing may be fast, but it also requires plenty of memory, which can quickly get expensive. Some users found that Apache Spark isn’t ideal for real-time analytics, while others found its data security capabilities lacking. It lacks automatic optimization and caching features, requiring some users to build the functionality themselves. Finally, Apache Spark may be designed intuitively, but it’s still a complicated tool with a steep learning curve.
Presto’s SQL optimization is also its primary limitation. It’s designed primarily to run SQL queries, while Apache Spark is suitable for a wider range of applications. This also means that Presto is at its best when the data it’s querying is already in SQL databases; although Presto can query and join data from multiple database types, you only get the highest speeds with SQL data. Additionally, Presto requires a lot of setup to run properly, with installation and configuration across many different nodes.
Pricing
Both Apache Spark and Presto are open-source and free.
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Presto is for interactive simple queries, where Hive is for reliable processing. If you have a fact-dim join, presto is great..however for fact-fact joins presto is not the solution.. Presto is a great replacement for proprietary technology like Vertica
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Linking, embedding links and adding images is easy enough.
Once you have become familiar with the interface, Presto becomes very quick & easy to use (but, you have to practice & repeat to know what you are doing - it is not as intuitive as one would hope).
Organizing & design is fairly simple with click & drag parameters.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Presto was not designed for large fact fact joins. This is by design as presto does not leverage disk and used memory for processing which in turn makes it fast.. However, this is a tradeoff..in an ideal world, people would like to use one system for all their use cases, and presto should get exhaustive by solving this problem.
Resource allocation is not similar to YARN and presto has a priority queue based query resource allocation..so a query that takes long takes longer...this might be alleviated by giving some more control back to the user to define priority/override.
UDF Support is not available in presto. You will have to write your own functions..while this is good for performance, it comes at a huge overhead of building exclusively for presto and not being interoperable with other systems like Hive, SparkSQL etc.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Presto is good for a templated design appeal. You cannot be too creative via this interface - but, the layout and options make the finalized visual product appealing to customers. The other design products I use are for different purposes and not really comparable to Presto.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
