Apache Spark Reviews

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.

The good performance. The nice graphical management console. The long list of ML algorithms.

We are able to solve problems, e.g., reporting on big data, that we were not able to tackle in the past.

Apache Spark provides very good performance The tuning phase is still tricky.

I've used it for 2 years.

We didn't have an issue with the deployment.

In the past we deployed Spark 1.3 to use Spark SQL but unfortunately one of our queries failed because of a bug fixed in following releases. Then we moved to Spark 1.6 but still some queries were failing when run against huge datasets. Now we are using version 2.1: it is more stable, it ensures better performances and the SQL/ML parts are reacher than before.

I've had no issues with the scalability.

I've never had to use customer service.

I've never had to use technical support.

The initial set-up is quite complex because you have to set-up many different configuration parameters that are deployment-specific. It is not trivial to set-up the correct configuration with so many variables involved.

In-house team. The setup itself is not a problem when you have just to test the system. The challenging part is discovering the optimal configuration needed to obtain a production system proving good performance.