In the race for interactive SQL in Big Data environments, there are two open source based front-runners, Impala and Hive with the Stinger project. Cloudera recently announced that Impala is up to 69 times faster than Hive 0.12 and can outperform DBMS. Other than raw speed, we take a look at other considerations in choosing a SQL engine for Hadoop and also Tez, an application framework for YARN.
LinkedIn’s DataFu project, a collection of libraries for Hadoop, has now officially entered the incubation status at the Apache Software Foundation (ASF) since the first week of January.
Google has acquired Nest, maker of smart thermostat and smoke detectors, for $3.2 billion in cash, making it another major data source that will help Google understand how people live.
Hadoop is definitely the platform of choice for Big Data analysis and computation. While data Volume, Variety and Velocity increases, Hadoop as a batch processing framework cannot cope with the requirement for real time analytics. Spark, Storm and the Lambda Architecture can help bridge the gap between batch and event based processing.
Presto, a technology from Facebook enabling interactive SQL queries on petabytes of data, has now taken a first step into mainstream adoption. Big Data startup Qubole has launched its Presto-as-a-Service alpha with integration to Amazon Web Services.
Big Data is a field where even a single millisecond loss can be significant over billions of events. Yet, languages often regarded as slow like Python have gained a lot of popularity in the past year. Recent articles and discussions in the Big Data community have started reigniting the debate around the choice of a programming language for data science and Big Data.
Curoverse and Tute Genomics secured $1.5 million each in seed funding in the past month aiming to bring gene sequencing to the masses. Illumina, Seven Bridges Genomics, Complete Genomics and others are offering researchers and private parties the opportunity to map the full genome sequence for a four figure quote. Illumina recently announced HiSeq X Ten, promising the long-awaited $1,000 genome.
Twitter has open sourced their MapReduce streaming framework, called Summingbird. Available under the Apache 2 license, Summingbird is a large-scale data processing system enabling developers to uniformly execute code in either batch-mode (Hadoop/MapReduce-based) or stream-mode (Storm-based) or a combination thereof, called hybrid mode.
2013 has been rich in announcements for new programs, degrees and grants for aspiring data scientists and Big Data practitioners.
In 2011 Trevor Eckhart found logs on his device that he believed were associated with Carrier iQ data. Our response at the time, which has since been confirmed by a detailed FTC investigation, is that the data collection logs were associated with and used by the manufacturer of the device, not Carrier iQ. They were not Carrier iQ logs.
Trifacta, a data analysis services platform, recently received VC investment to advance on their efforts of making data wrangling easier for data analysts. The goal is to collect, cleanse and munge data in a fraction of the time and effort it currently takes.
Qubole, a managed Hadoop-as-a-Service offering is now available on Google Compute Engine (GCE). Qubole was so far only available on Amazon's AWS and this announcement follows only a few days after Google releasing GCE into general availability.
The MapReduce paradigm is not always ideal when dealing with large computationally intensive algorithms. A small team of entrepreneurs is building a product called ParallelX to solve that bottleneck by harnessing the power of GPUs to give Hadoop jobs a significant boost.
EC2 users can now automate the deployment of Apache Mesos, an open-source tool to share cluster resources between multiple data processing frameworks, at scale through a new web service called Elastic Mesos provided by Big Data startup Mesosphere.
Martin Fowler writes about the opposite of Big Data, Datensparsamkeit. This German word roughly translates to “data austerity” or simply “not storing more than you need”.