BigData: All content tagged as BigData in NoSQL databases and polyglot persistence
Matt Asay writes in the ReadWrite article that Hadoop is not replacing existing data warehouses, but it’s taking all new projects:
Hadoop (and its kissing cousin, the NoSQL database) isn’t replacing legacy technology so much as it’s usurping its place in modern workloads. This means enterprises will end up supporting both legacy technology and Hadoop/NoSQL to manage both existing and new workloads […]
Of course, given “the effective price of core Hadoop distribution software and support services is nearly zero” at this point, as Jeff Kelly highlights, more and more workloads will gravitate to Hadoop. So while data warehouse vendors aren’t dead—they’re not even gasping for breath—they risk being left behind for modern data workloads if they don’t quickly embrace Hadoop and other 21st Century data infrastructure.
On his blog, Timo Elliott makes sure that there’s some SAP in that future picture and uses their Hadoop partner, Hortonworks to depict it:
No. Ignoring the many advantages of Hadoop would be dumb. But it would be just as dumb to ignore the other revolutionary technology breakthroughs in the DW space. In particular, new in- memory processing opportunities have created a brand-new category that Gartner calls “hybrid transactional/analytic platforms” (HTAP)
The future I’d like to see is the one where:
- there is an integrated data platform. Note that in this ideal world, integrated does not mean any form of ETL
- it supports and runs in isolation different workloads from online transactions and bulk upload to various forms of analytics
- data is stored on dedicated mediums (spinning disks, flash, memory) depending on the workloads that touch it
- data would move between these storage mediums automatically, but the platform would allow fine tuning for maintaining the SLAs of the different components
Original title and link: Three opinions about the future of Hadoop and Data Warehouse ( ©myNoSQL)
Update: I’d like to thank the people that pointed out in the comment thread that I’ve messed up quite a few aspects in my comments about the report. I don’t believe in taking down posts that have been out for a while, so please be warned that basically this article can be ignored.
Thank you and my apologies for those comments that were a misinterpretation of the report..
This is the Q1 2014 Forrester Wave for Hadoop:
A couple of thoughts:
Cloudera, Hortonworks, MapR are positioned very (very) close.
- Hortonworks is position closer to the top right meaning they report more customers/larger install base
MapR is higher on the vertical axis meaning that MapR’s strategy is slightly better.
For me, MapR’s strategy can be briefly summarized as:
- address some of the limitations in the Hadoop ecosystem
- provide API-compatible products for major components of the Hadoop ecosystem
- use these Apache product (trade marked) names to advertise their products
I think the 1st point above explains the better positioning of MapR’s current offering.
Even if Cloudera has been the first pure-play Hadoop distribution it’s positioned behind behind both Hortonworks and MapR.
IBM has the largest market presence. That’s a big surprise as I’m very rarely hearing clear messages from IBM.
IBM and Pivotal Software are considered to have the strongest strategy. That’s another interesting point in Forrester’s report. Except the fact that IBM has a ton of data products and that Pivotal Software is offering more than Hadoop, I don’t know what exactly explains this position.
The Forrester report Strategy positioning is based on quantifying the following categories: Licensing and pricing, Ability to execute, Product road map, Customer support. IBM and Pivotal are ranked the first in all these categories (with maximum marks for the last 3). As a comparison Hortonworks has 3/5 for Ability to execute — this must be related only to budget; Cloudera has 3/5 for both Ability to execute and Customer support.
Pivotal is the 3rd last in terms of current offering. I guess my hypothesis for ranking Pivotal as 1st in terms of strategy is wrong.
Microsoft who through the collaboration with Hortonworks came up with HDInsight, which basically enabled Hadoop for Excel and its data warehouse offering, it positioned the 2nd last on all 3 axes.
No one seems to love Microsoft anymore.
While not a pure Hadoop player, DataStax has been offering the DataStax Enterprise platform that includes support for analytics through Hadoop and search through Solr for at least 2 years. That’s actually way before anyone else from the group of companies in the Forrester’s report had anything similar1.
This report focuses only on “general-purpose Hadoop solutions based on a differentiated, commercial Hadoop distribution”.
You can download the report after registering on Hortonwork’s site: here.
DataStax is my employer. But what I wrote is a pure fact. ↩
Original title and link: The Forrester Wave for Hadoop market ( ©myNoSQL)
A great matrix of the different analytics use cases across industries in Hortonworks’s post “Enterprise Hadoop and the Journey to a Data Lake“:
The data type column section covers multiple dimensions of data. And the authors took a conservative approach for the structured and unstructured categories (in the sense that they marked very few categories as unstructured).
A couple of interesting exercises that can be done using this matrix as an input:
figure out how adding data from different categories to a specific use case would benefit it. One obvious example is: how would Telecom companies benefit from adding to their infrastructure analysis social data?
Building on the above, decide what tools exist to help with this extra scenario.
can one use case from an industry be applied to a different industry to disrupt it?
What would be the quickest road to accomplish it?
Original title and link: Examples of analytics applications across industries ( ©myNoSQL)