Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.
N/A
SingleStore
Score 9.7 out of 10
N/A
SingleStore aims to deliver the world’s fastest distributed SQL database for data-intensive applications: SingleStoreDB, which combines transactional + analytical workloads in a single platform.
Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.
SingleStore HTAP engine is well suited for real-time analytics, fast ingestion, scaling OLTP system like MySQL. When you need to run reports or perform aggregates on billions of rows and you get result in seconds, you cannot get this experience with other OLTP engines. I wish DBtLab was a little more developer and supported for SingleStore. This would allow to perform better data transformation. You can use stored procedures, but DBTLabs has become a standard for dimensional modeling in data warehousing projects. This is probably why SingleStore has trouble piercing in the data warehouse world. It is definately capable to compete with Snowflake when it comes to scalability, query performance, data compression, but Snowflake has ravaged the data warehouse market in few years and large corporations have already invested lots of money in migrating into Snowflake. The SingleStore community needs to grow. Everyone who uses SingleStore loves it.
We wish the product had better support for High Availability of the aggregator. Currently the indexes generated by the two different aggregators are not in the same sequential space and so our apps have more burden to deal with HA.
More tools for debugging issues such as high memory usage would be good.
The price was the one that kept us away from purchasing for the first few years. Now we are able to afford due to a promotion that gives it at 25% of the list price. Not sure if we'll continue after the promotion offer expires in another 2 years.
[Until it is] supported on AWS ECS containers, I will reserve a higher rating for SingleStore. Right now it works well on EC2 and serves our current purpose, [but] would look forward to seeing SingleStore respond to our urge of feature in a shorter time period with high quality and security.
SingleStore's performance is incredible. Our predictive algorithms went from taking 24-48 HOURS down to 15 minutes allowing our team to run those much more frequently. Previously, we were limited to about 60 requests per minute due to table locks. Implementing columnstore on SingleStore allowed us to receive 1000 requests per minute.
Very responsive to trouble tickets - Often, I think, the SingleStore's monitoring systems have already alerted the engineers by the time I get around to writing a ticket (about 10 - 20 mins after we see a problem). I feel like things are escalated nicely and SingleStore takes resolving trouble tickets seriously. Also SingleStore follows up after incidents to with a post mortem and actionable takaways to improve the product. Very satisfied here.
We allowed 2-3 months for a thorough evaluation. We saw pretty quickly that we were likely to pick SingleStore, so we ported some of our stored procedures to SingleStore in order to take a deeper look. Two SingleStore people worked closely with us to ensure that we did not have any blocking problems. It all went remarkably smoothly.
Apache Pig might help to start things faster at first and it was one of the best tool years back but it lacks important features that are needed in the data engineering world right now. Pig also has a steeper learning curve since it uses a proprietary language compared to Spark which can be coded with Python, Java.
Timescale was the biggest alternative option we looked at for SingleStore, however the requirement to learn a new syntax (due to not being SQL compatible) was our biggest pain point. Supporting a new language would require alterations to the Laravel framework, as this only offered SQL integration out of the box. This alteration would be time consuming and would limit our scope to future hiring due to the new syntax.
Higher learning curve than other similar technologies so on-boarding new engineers or change ownership of Apache Pig code tends to be a bit of a headache
Once the language is learned and understood it can be relatively straightforward to write simple Pig scripts so development can go relatively quickly with a skilled team
As distributed technologies grow and improve, overall Apache Pig feels left in the dust and is more legacy code to support than something to actively develop with.
As the overall performance and functionality were expanded, we are able to deliver our data much faster than before, which increases the demand for data.
Metadata is available in the platform by default, like metadata on the pipelines. Also, the information schema has lots of metadata, making it easy to load our assets to the data catalog.
