IBM Analytics Engine vs. Looker

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM Analytics Engine
Score 8.7 out of 10
N/A
IBM BigInsights is an analytics and data visualization tool leveraging hadoop.N/A
Looker
Score 8.3 out of 10
N/A
Looker is a BI application with an analytics-oriented application server that sits on top of relational data stores. It includes an end-user interface for exploring data, a reusable development paradigm for data discovery, and an API for supporting data in other systems.N/A
Pricing
IBM Analytics EngineLooker
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
IBM Analytics EngineLooker
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeRequired
Additional DetailsMust contact sales team for pricing.
More Pricing Information
Community Pulse
IBM Analytics EngineLooker
Top Pros
Top Cons
Features
IBM Analytics EngineLooker
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
IBM Analytics Engine
-
Ratings
Looker
8.1
97 Ratings
1% below category average
Pixel Perfect reports00 Ratings7.680 Ratings
Customizable dashboards00 Ratings8.896 Ratings
Report Formatting Templates00 Ratings7.982 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
IBM Analytics Engine
-
Ratings
Looker
8.1
97 Ratings
0% above category average
Drill-down analysis00 Ratings8.294 Ratings
Formatting capabilities00 Ratings7.495 Ratings
Integration with R or other statistical packages00 Ratings8.040 Ratings
Report sharing and collaboration00 Ratings8.697 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
IBM Analytics Engine
-
Ratings
Looker
8.6
93 Ratings
3% above category average
Publish to Web00 Ratings8.577 Ratings
Publish to PDF00 Ratings8.783 Ratings
Report Versioning00 Ratings8.263 Ratings
Report Delivery Scheduling00 Ratings8.983 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
IBM Analytics Engine
-
Ratings
Looker
6.8
94 Ratings
17% below category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings8.192 Ratings
Location Analytics / Geographic Visualization00 Ratings7.581 Ratings
Predictive Analytics00 Ratings4.66 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
IBM Analytics Engine
-
Ratings
Looker
8.5
93 Ratings
0% below category average
Multi-User Support (named login)00 Ratings8.988 Ratings
Role-Based Security Model00 Ratings8.381 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings8.688 Ratings
Report-Level Access Control00 Ratings8.429 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
IBM Analytics Engine
-
Ratings
Looker
5.8
69 Ratings
31% below category average
Responsive Design for Web Access00 Ratings6.766 Ratings
Mobile Application00 Ratings5.01 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings6.560 Ratings
Best Alternatives
IBM Analytics EngineLooker
Small Businesses

No answers on this topic

SAP Crystal
SAP Crystal
Score 9.0 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Reveal
Reveal
Score 10.0 out of 10
Enterprises
Azure Data Lake Storage
Azure Data Lake Storage
Score 8.4 out of 10
Jaspersoft Community Edition
Jaspersoft Community Edition
Score 9.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
IBM Analytics EngineLooker
Likelihood to Recommend
9.5
(9 ratings)
8.3
(98 ratings)
Likelihood to Renew
-
(0 ratings)
9.0
(4 ratings)
Usability
-
(0 ratings)
8.8
(12 ratings)
Support Rating
-
(0 ratings)
8.8
(14 ratings)
User Testimonials
IBM Analytics EngineLooker
Likelihood to Recommend
IBM
  • Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
  • But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.
Read full review
Google
Quick dashboards from Google Sheets - Easier to do the graphs than in Google Sheets - Operational dashboards to be used in the day-to-day work - It is good both for retrospective data and to do a pulse check of the current status - Better for not giant amounts of data and not multiple data sources. - If you need a lot of graphs to be displayed on the same page, it can be a bit glitchy during configuration (then the use works fine).
Read full review
Pros
IBM
  • 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.
Read full review
Google
  • Filtering - you can filter across different dimensions and metrics to get a more specific "cut" of data
  • Refreshing - data automatically ingests into Looker which allows reports to be updated and backfilled in real time
  • Conditional Reporting - you can leverage Looker's reporting features to flag when a given metric or KPI falls below or above a specified threshold. For example, if you had a daily sales benchmark in a SAAS organization, you could use Looker to flag whenever daily sales falls above or below the benchmark
Read full review
Cons
IBM
  • 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.
Read full review
Google
  • Looker is less graphical or pictorial which makes it less attractive
  • Consumes a lot of memory when there are multiple rows and columns, impacts performance too
  • At times when we download huge chunks of raw data from Looker dashbords, the time taken to prepare the file is enormous - The user fails to understand if Looker has frozen or if the data is getting prepared in the background for downloading. In turn, user ends up triggering multiple downloads
Read full review
Likelihood to Renew
IBM
No answers on this topic
Google
We've been very happy with Looker so far, and all teams in the organization are starting to see its value, and use it on a frequent basis. It has quickly become our accessible "source of truth" for all data/metrics.
Read full review
Usability
IBM
No answers on this topic
Google
Looker is relatively easy to use, even as it is set up. The customers for the front-end only have issues with the initial setup for looker ml creations. Other "looks" are relatively easy to set up, depending on the ETL and the data which is coming into Looker on a regular basis.
Read full review
Support Rating
IBM
No answers on this topic
Google
Never had to work with support for issues. Any questions we had, they would respond promptly and clearly. The one-time setup was easy, by reading documentation. If the feature is not supported, they will add a feature request. In this case, LDAP support was requested over OKTA. They are looking into it.
Read full review
Alternatives Considered
IBM
We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.
Read full review
Google
Looker is an off-the-shelf, free tool for Google business users. Other than the internal cost of time to build, we had no costs to set up what we needed to do. Knowledge sharing internally and using templates greatly reduced this cost, making the overall cost very low.
Read full review
Return on Investment
IBM
  • This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
  • IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
  • The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.
Read full review
Google
  • Allowing others to self-serve their own analytics and connect it to Looker simply and easily has helped unblock the central data team so they can instead focus on validated dashboards whilst stakeholders manage their day-to-day analysis themselves. Countless engineering hours have been freed up by not having to manage every user permission for each BI tool; we have a BYOBI approach; Bring Your Own BI
  • Creation and management of a semantic layer (LookML =Looker Modeling Language ) allows peoples sandboxes and production databases to become clutter free. Minor adjustments, conditional fields, and even some modelling can all be done in LookML which doesn't need oversight or governance from the central data team.
  • LookML, specifying drilldown fields and their sub-queries, as well as generally creating dynamic parameters with Liquid are all great features, but can have a steep learning curve. it may take some time to understand how to create this middle layer correctly, or even pose a risk of inheriting complex code from another source which can be unmaintainable if it becomes too big. Some level of governance is recommended if Looker is used by a large number of editors.
Read full review
ScreenShots

Looker Screenshots

Screenshot of a Looker dashboard with a geo chart.