- Title: Array Databases: The Next Big Thing in Data Analytics?
- Authors: Alex Woodie
Array Databases: The Next Big Thing in Data Analytics?
There’s no lack of database choices when it comes to building your next big data analytics platform. Relational data stores, column-oriented databases, in-memory data grids, graph databases, scale-out NewSQL systems, and Hadoop will all get time in the sun. But according to database pioneer Mike Stonebraker, none of these hold a candle to array databases when it comes to running complex analytics on big data sets.
Just a heads-up: Be careful with the word “Hadoop” around Stonebraker. “Please don’t use the word ‘Hadoop,’” the father of Postgres, Ingres, Vertica, VoltDB, and now SciDB says in an interview with Datanami. “That means all kinds of things to all kinds of people.”
Hadoop, as Stonebraker sees it, is not the big data application and analytics platform of the future, at least in the way that Hadoop distributors are positioning it today. The blunt-speaking technologist breaks the big yellow elephant down into a three-tiered architecture, with the Hadoop Distributed File System (HDFS) at the bottom, MapReduce in the middle, and an application based on Impala, Hive, Pig, or another package running on the top.
As SQL interfaces, Impala and Hive provide much the same capability that traditional data warehouses have been providing for years, he says. But the MapReduce layer—that has almost no practical value in the big data analytics market today, he says.
“The Google guys have to be just laughing in their beer right now because they invented MapReduce a decade ago to serve the data storage needs of the Google crawl of the Internet… and moved all of that to Big Table,” Stonebraker says. “Why did they do that? Because MapReduce is a batch engine and they wanted their crawl database to be updatable, as they wanted to get Twitter feeds into it, in real time.”
“So Google has effectively abandoned MapReduce for the application for which it was originally designed, and has moved on,” he continues. “So what’s happened is a decade later, the rest of the world seems to think, well because Google designed MapReduce, it must be good. And the answer is, it’s not. It’s not good for much of anything.”
Mike Stonebraker ———————————————————————————————————–
Once you take MapReduce out of the Hadoop equation, you’re left with just a file system, HDFS. “It’s just like the Linux file system or any other file system,” he says. “So everyone on the planet is going to run on HDFS–and, for that matter, support a translator from Hive into whatever their query language is. So if you want either of those interfaces, everyone is going to give them to you.”
In Stonebraker’s vision of the future of big data analytics, the upstart Hadoop vendors of the world (Cloudera, Hortonworks, Facebook, etc.) will largely transform their offerings into SQL-compatible data warehouses, and sell those against the traditional data warehouse vendors with fast, parallelized column-oriented databases, including Teradata, IBM, Greenplum, HP Vertica, and Amazon Redshift (which is powered by Actian’s ParAccel database).
Only five percent of the data analytics market will be running on mappers and reducers, he says. “The other 95 percent of the market wants SQL,” he says. “What the Hadoop vendors have figured out is what the database vendors have known for two decades, which is the stack to implement SQL does not include anything that looks like MapReduce.”
As Cloudera and friends battle Teradata and friends for supremacy in the general-purpose data warehousing and business intelligence arena, the space around them increasingly will be occupied by a crop of special purpose engines to operate on big data sets. “Special purpose engines are going to be way faster than general purpose ones,” he says. “XML engines will do fine. Graph engines will do fine. Array engines will do fine.”
Array engines, such as the SciDB offering that Stonebraker developed for his latest venture, Paradigm4, are specially designed to run complex analytics, such as correlating, clustering, predictive modeling, and machine learning, he says.
“In my opinion, what’s going to happen over the next five years is that everyone is going to move from business intelligence to data science, and this data will be a sea change from what I’ll call stupid analytics, to what I’ll call smart analytics, which is correlations, data clustering, predictive modeling, data mining, Bayes classification,” Stonebraker says. “All of these words mean complex analytics. All that stuff is defined on arrays, and none of it is in SQL. So the world will move to smart analytics from stupid analytics, and that’s where we are.”
It’s true that other types of databases support array data types. But the folks at Paradigm4 argue that, as data sets get bigger and the analytics become more demanding, the disadvantages of moving the data back and forth between relational and array data types will outweigh the advantages of storing them from the beginning in a native multi-dimensional array data model, such as SciDB’s.
“What people have tended to do historically is they fetch the data they’re interested in….and send it across the wire into some stat package–think R, or SAS, or SPSS–then they run the statistics in the stat package and put the answer back in the database system,” Stonebraker says. “I’ve talked to lots of people who do that, and they’re all tearing their hair out because they have to learn two different systems. They have to copy the world over the wire, and no one wants to do that. So what we support is doing data management and doing complex analytics without having to move the data.”
Stonebraker, who continues to teach at MIT, co-founded Paradigm4 in the Boston area in 2008 to attack this market opportunity. The company, which is headed by CEO Marilyn Matz and also includes chief architect Paul Brown, is getting particular traction in specific use cases, including the analysis of genetic sequencing data, sensor data, financial data and geospatial data.
Big pharmaceutical companies are interested in how SciDB can help them match genetic information (the genotype) to the manifestation of disease in the human body (phenotype), and so some cross-correlation among age, race, income level, and geographic location while they’re at it. “The cost of sequencing any human is down around $1,000, and millions of people are going to get sequenced over the next few years,” Stonebraker says. “And the gleam in all the pharmaceutical companies’ eyes is to find out what in your genomic makeup correlates with diseases.”
If SQL and MapReduce is the past and hardcore data science is the future of data analytics, then Stonebraker and SciDB might be on to something.