Apache Spark Reviews

We primarily use the solution for security analytics.

The scalability has been the most valuable aspect of the solution.

The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive. 

The 2.3 version is quite stable. All of our customers use it, there are around 100,000+ users, and it runs 24/7.

The scalability is very good.

You actually buy Cloudera along with it. You don't really get any support, except you need support.

In previous companies, we used MySQL platform and solutions like ArcSight and Splunk. We switched for scalability. MySQL wasn't going to scale, and we don't use Splunk at this company.

The initial setup was complex. It is a complex tool. It's a lot to do with how you will use it. There is a lot to set up. They need to put a lot of scripts to it. There's nearly 60 to set up. When you set up the cloud, it takes about a day to set up. If you set it up on-premise, you know, on hardware, it only takes about a week.

I would rate this solution eight out of 10. 

Streaming telematics data.

It's a better MR, supports streaming and micro-batch, and supports Spark ML and Spark SQL.

It supports streaming and micro-batch.

Better data lineage support.

Ingesting billions of rows of data all day.

Spark on AWS is not that cost-effective as memory is expensive and you cannot customize hardware in AWS. If you want more memory, you have to pay for more CPUs too in AWS.

Powerful language.

It is like going back to the '80s for the complicated coding that is required to write efficient programs.

Used for building big data platforms for processing huge volumes of data. Additionally, streaming data is critical.

It provides a scalable machine learning library so that we can train and predict user behavior for promotion purposes.

Machine learning, real time streaming, and data processing are fantastic, as well as the resilient or fault tolerant feature.

I would suggest for it to support more programming languages, and also provide an internal scheduler to schedule spark jobs with monitoring capability.

Organisations can now harness richer data sets and benefit from use cases, which add value to their business functions.

Distributed in memory processing. Some of the algorithms are resource heavy and executing this requires a lot of RAM and CPU. With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware.

Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing.

At times when users do not know how to use Spark and request a lot of resources, then the underlying JVMs can crash, which is a big sense of worry. 

No issues.

DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort.

We developed a tool for data ingestion from HDFS->Raw->L1 layer with data quality checks, putting data to elastic search, performing CDC.

Dynamic DataFrame options are not yet available.

One and a half years.

No.

No.

Spark gives the flexibility for developing custom applications.

The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics. The community is growing and hence executing ML in a distributed fashion is quite good.

Previously we were using Hadoop MapReduce to reduce the Google Ngrams (3TB), which took us approximately five days on our cluster. After using Spark, we were able to accomplish this task within hours.

This product is already improving as the community is developing it rapidly. More ML based algorithms should be added to it, to make it algorithmic-rich for developers.

Two and a half years.

No, I did not encounter any problems with the stability. It is also quite backwards compatible.

No I did not as of now, it is quite scalable. Using simple scripts you can add as many workers as you want.

This is a very good product for the big data analytics and integrates well with other parts like Machine Learning and graph analytics.

ETL and streaming capabilities.

Made Big Data processing more convenient and a uniform framework adds to efficiency of usage since the same framework can be used for batch and stream processing.

Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet).

I have used Spark since its inception in March 2015, from Spark 1.1 onwards.

Currently, I use 2.2 extensively.

Yes, occasionally with different APIs.

No.

Since we were using the Open Source version of Apache Spark, without the Databricks support, we never used technical support form Databricks.

Yes we used Hive, Pig, and Storm. Having everything in the same framework has helped us out a lot.

Yes, we considered other big data products in the Big Data Ecosystem.

Go for it.