We’re Post-Hadoop — Hub(ba) Hub(ba)

Chris Dima
SlamData CEO and Co-Founder Jeff Carr on Hadoop: “It was the classic example of when you have nothing, anything is better.”

This week we saw something nobody would have expected to see a year ago: a Hadoop-free Strata conference. According to Datanami’s Managing Editor Alex Woodie who was on site:

“It was an auspicious absence, to be sure. Making a big yellow elephant essentially vanish in the space of half a year is not an easy feat. But the fact remains that what used to be the rallying point for an entire industry has essentially been reduced to an afterthought. Cloudera, which puts on the show with O’Reilly Media, scarcely even mentioned Hadoop.

I talked to Jeff Carr, SlamData Co-Founder and CEO, who saw first-hand as Hadoop started gaining popularity 10 years ago: “It was the classic example of when you have nothing, anything is better.”

Request A Demo

No ETL. No Mapping. No Stale Extracts.

“The idea of large scale, highly scalable, cost-effective storage was unattainable — so it was easy to see why so many companies went down that road,“ said Carr.

Hadoop Is Past It's Peak. It's All Data Hub Now

The One-Way Parking Lot

If you ask your nearest big data engineer or data scientist, everyone will say that saw it coming. Is this the typical 20/20 hindsight everyone has or was there a clear and obvious problem?

Jeff Carr: “It didn’t take long to realize that Hadoop was really good at storing data it just wasn’t good at doing much else (see Are You Drowning In the Data Lake?). So what’s data without the insight?”

You’ve heard about MapReduce queries on Hadoop that took weeks to run haven’t you? It was reality.

TRENDING BLOGS

Now Available: the Definitive Guide To JOINs On MongoDB

Lots of business come to us looking for help with doing BI on MongoDB. Specifically, many people want to just do what they’ve always done: query data with SQL. Here's the definitive guide!

Webinar Replay: Plug-And-Play Analytics for Your SaaS App Built On MongoDB

Check out the replay of this webinar to learn how US Mobile uses SlamData to deliver interactive reporting across its business.

The Five Money-Saving Tricks MongoDB Doesn’t Want You To Know

If you listen to your friendly MongoDB sales rep, it's easy to think they are a one-stop shop for all things MongoDB.

The New Age of Data

For me, one of the most compelling reasons to work in “data” (not saying ‘big data’ anymore) is this: time-to-insight is closing in on ‘now’. People who ran businesses used to wait weeks or months for paper reports to land on their desks — the result of often questionable and undocumented data prep methodologies. That’s not far from multi-week MapReduce jobs. So, looking back, this is kinda the timeline:

  1. Amass tons of data
  2. Find a place to put it
  3. Realize what good is tons of parked data if business analysts can’t easily get to it
  4. Onslaught of Hadoop/Spark solutions
  5. ?

#5 is now.

We’re post Hadoop. We may even be post Spark. Not really, but we’re definitely not cornered into using Spark. Spark still requires developers.

Pass Hadoop Collect $200

Hadoop is no longer a requirement. Gartner’s 2017 Hype Cycle for Data Management lays it out sans sugar:

Get Updates And News From SlamData

“Hadoop distributions are deemed to be obsolete… because the complexity and questionable usefulness of the entire Hadoop stack is causing many organizations to reconsider its role in their information infrastructure.”

So now that we have the “official” declaration from Gartner, let’s talk options.

For storage, there’s a whole lot of NoSQL databases that can store data and scale without much effort: MongoDB, Couchbase, Cassandra
Even the so-called “NewSQL” solutions like CockroachDB offer compelling alternatives. I mean, there’s literally tons of options.

But to most directly hit the problem on the head — Hadoop’s double-whammy of “Ugh” — you need to wrap your head around a data hub. It’s what I call the big data utility bucket. It’s easy, it’s fast, it’s hardly even technical.

Hubba Hubba

A data hub is a bucket of data. Throw anything you want in there. Connect feeds even. Then connect SlamData. Without doing any prep work, you’ll get to see everything in your hub — just waiting for you to explore and pivot table your way to just the right view — and then slide into home plate with killer, interactive report that you can share anywhere. Most folks call the first part of this awesomeness a data hub.

But beware. If you’re just throwing data into a bucket and then unleashing a bunch of python developers on it then you’ve just exchanged on problem for another.

News, Analysis and Blogs

What Our Customers Are Saying

v

We use SlamData to build custom reports and have found the tool is exceptionally easy to use and very powerful. We recently needed to engage the support team and we were very pleased with the turn-around time and the quality of support that we received.

Troy Thompson
Director of Software Engineering
Intermap Technologies, Inc.

v

When our company migrated from SQL database to MongoDB, all our query tools became obsolete. SlamData saved the day! I was able to easily write SQL2 queries. Plus the sharing, charting, and interactive reports were a game changer.

Michael Melmed
VP, Ops and Strategy
US Mobile

v

Slamdata helped shine the light on how our new product was being used. The support staff was awesome and we saved engineering cycles in building all the analytics in-house. I am using it to change the mindset in the teams and shift the focus from product launches to product landings

Engineering Lead
Cisco Systems

WHITEPAPER

The Characteristics of NoSQL Analytics Systems

  • The Nature of NoSQL Data
    • APIs
    • NoSQL Databases
    • Big Data
    • A Generic Data Model for NoSQL
  • Approaches to NoSQL Analytics
    • Coding & ETL
    • Hadoop
    • Real-Time Analytics
    • Relational Model Virtualization
    • First-Class NoSQL Analytics
  • Characteristics of NoSQL Analytics Systems
    • Generic Data Model
    • Isomorphic Data Model
    • Multi-Dimensionality
    • Unified Schema/Data
    • Post-Relational
    • Polymorphic Queries
    • Dynamic Type Discovery & Conversion
    • Structural Patterns

What's People Are Saying

© 2017 SlamData, Inc.

Do NOT follow this link or you will be banned from the site!

SlamData Provides Missing Platform for NoSQL Data Insight

This case study documents the return on investment, performance enhancements, and efficiency gains experienced by US Mobile resulting from its SlamData implementation. 
Download Case Study Now
The study was conducted by Constellation Research and published on June 25, 2017.
close-link
Click Me
Tweet
Share
Share
+1
Reddit
Buffer