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January 14, 2021
We have recently started utilizing IBM Analytics & so far it has been a good experience. Our Data Intelligence team along with the DevOps team makes use of this tool as they deal with an enormous amount of data on a daily basis. The majority use it analyze the overall performance of all the applications related to Hadoop by gauging the datasets & check for any inconsistency in their behavior.
- Easy-to-use User interface with featured pack functionalities.
- Scalable & customizable according to user needs.
- Powerful yet simple cluster administration.
- Affordable Pricing.
- Could improve upon step-by-step instructions & make it more comprehensive.
- Can be more prompt with their customer service.
- Could come up with an e learning module for using the tool.
April 28, 2021
We use [IBM Analytics] to provision, manage, run and retire Apache Hadoop and Apache Spark clusters. With its calculation engine, this enterprise performance
management solution helps the users move beyond the limits of
spreadsheets, automating the planning process to drive faster, more
accurate results. We use it to unify data sources into one single
repository, because it enables users to build sophisticated, multidimensional
models that drive forecasts. So far we haven't had any problems with the software.
management solution helps the users move beyond the limits of
spreadsheets, automating the planning process to drive faster, more
accurate results. We use it to unify data sources into one single
repository, because it enables users to build sophisticated, multidimensional
models that drive forecasts. So far we haven't had any problems with the software.
- Jobs with Spark, Hadoop, or Hive queries are rapidly attained
- Can collect, organize and analyze your data accurately
- You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.
- I find most of the functions user friendly and most importantly best fits in multi business setup and users easily adapt to the system. Also, you get a lot done in a very short period of time.
February 01, 2018
- We used it for a research project to implement big data analysis on a YELP dataset from 2017 to answer a sales related question. We used the default Ambari file browser to upload the data, Hadoop ecosystem for data cleaning, Hive to query the dataset followed by TABLEAU for visualization.
- Simple to implement
- Good support from IBM
- Easy to use
- Widely used for Big data implementation
- Even for L4 level support, assistance could have been more prompt. Since we are doing this for master level research all the students faced an issue when there was a MIGRATION to a newer version of the analytical engine, which doesn't let us take cmd line.
- The explanation was not clear enough when a problem is resolved followed by a service request. Apart from being confidential, it's better for the students to understand why an issue occurs and what steps were take to resolve it.
March 07, 2019
We have just begun to explore very large data sets and gave the IBM Analytics Engine a test to see how it would work for us. We were quite surprised at how complete and easy it was to set up and manage multiple Spark and Hadoop clusters. The separation of computing and storage really drives the performance up and gives you a better architecture with which to manage your overall big data plans.
- It’s easy to integrate if you are already on IBM cloud, and even if you are not you can still explore the whole package with their free Lite plan and some service credits they offer.
- Unlimited clusters without any performance degradation is a nice selling point. The smaller cluster size seems to work since it is only the computer, and not mixed with storage.
- The connection to Watson Studio helps manage your jobs as you submit them to the cluster, and this is a nice easy relationship with Analytics Engine.
- Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
- Bundling of the Cloud Object Storage should be included with the Analytics Engine.
- The inability to add your own Hadoop stack components has made some transfers a little more complex.
February 09, 2019
As far as I know, we are the only department in our rather large organization that is currently using the IBM Analytics Engine. We needed a relatively low-cost solution to perform analysis without a huge amount of maintenance or upkeep required. It also allowed us to perform tasks we did not foresee, such as analyze data across multiple applications.
- Ease of use / user interface
- Reasonable pricing
- Ability to build and deploy clusters very quickly... within minutes
- I would like to see a more robust version of their online help
- The speed of their business support is adequate, but I kind of expect more from such a powerhouse.
- Problems with duration of cluster life
December 31, 2018
IBM Analytics Engine is being used by my organization to analyze our data across multiple applications. We use it primarily to gather data to be used to analyze application performance and reliability. This data allows us to ensure that we are delivering the best possible application performance to our customers and business partners across all channels.
- We are able to build and deploy clusters within minutes to simplify user experience and increase scalability and reliability.
- We are able to scale and compute on-demand to handle newer workloads like machine learning.
- We really like that we are able to access and administer the application via multiple interfaces.
- Some of the documentation can be overwhelming and very technical. We would love to have some documentation written for non-technical people.
- We would like to see better communication from IBM regarding upgrades and patching cycles of the product.
- The learning curve for this product is steep. This is definitely not a product that will be used right out of the box.
May 14, 2018
We used IBM Analytics Engine for one of the public sector clients who have to deal with massive amounts of data coming from their mobile app and centralized internet web applications. They were previously running a custom solution developed by one of the big fours and were unfortunate enough to have lost a data due to infrastructure failure. The high-level idea was to separate computing and storage and simpler administration of clusters without losing the analytics capabilities.
- Separation of computing and storage without losing analytics capability
- Simple and straightforward cluster administration
- Relatively simpler navigation as compared to the alternatives
- Business support is not half as good as it should be. Takes a while to provide support to even high priority tickets.
- Lack of proper documentation on features and how they can be leveraged for maximum ROI.
I go to California state university, Los Angles for a master in information systems. Our computer science department uses IBM Analytics Engine along with the students to give them hands on experience with a real time tool. It works pretty great. I used IBM Analytics Engine to learn about Hadoop and soon will learn about Spark SQL and so on.
- Compact clusters. Via the trial versions that I use, I have a great insight of how different organizations create clusters and manage data
- Fast: the load time at registration is a bit slow. But after creating the first cluster, everything went way faster
- Realistic: hands on experience
- Reasonable cost
- Lagging at some point. Inconsistency in maintaining and running clusters.
- Should allow the cluster to last longer before refreshing
- It’s a bit confusing to navigate through the site. Sometime I thought I hit the right section then it lead me to another one.
- The log in page is inconsistent. I could find 3 different IBM log in page via Google. And then have to click many things that will lead to where I want.
It was used by my department only. It helped to analyze our program outcome throughout the 6 months that I was working with that program. It was really helpful, and shortened our analysis procedure by a lot. Moreover, it saved the company a lot of labor and time.
- easy use of interface
- clear instructions of where everything is
- the two week use of nodes
- some feature updates without clear instructions
- better online help
IBM Analytics Engine Scorecard Summary
What is IBM Analytics Engine?
IBM Analytics Engine is a Hadoop and Spark service allowing users to rapidly build and deploy analytics applications.
Categories: Data Discovery and Visualization, Hadoop-Related
IBM Analytics Engine Technical Details
| Operating Systems: | Unspecified |
|---|---|
| Mobile Application: | No |
Frequently Asked Questions
What is IBM Analytics Engine?
IBM BigInsights is an analytics and data visualization tool leveraging hadoop.
What are IBM Analytics Engine's top competitors?
Apache Spark, Amazon EMR (Elastic MapReduce), and Google BigQuery are common alternatives for IBM Analytics Engine.
Who uses IBM Analytics Engine?
The most common users of IBM Analytics Engine are from Enterprises and the Information Technology & Services industry.