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Algorithms supported by Spark can be effectively used in this step for complex tasks such as machine learning and graph processing as well.
#Big data analysis with apache spark software
Joab Jackson covers enterprise software and general technology breaking news for The IDG News Service.
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"We've had multiple candidates out there say that they have seen multiple exciting Spark projects," Ghodsi said. Programmers are often are asked about their Spark chops. We will see many more products and services based on Spark next year, predicted Databricks' Ghodsi. "Part of the attraction of Spark is that it has a pretty nice API that makes it accessible to use for developers and engineers," said Reynold Xin, a Databricks co-founder. Developers build applications off of Spark using either Python, Java or the Scala programming languages. Spark can be used in conjunction with Hadoop, to analyze data on the Hadoop File System (HDFS), or it can be run on its own. Core contributors include engineers and developers from companies such as Intel, Yahoo, Groupon, Alibaba and Mint. Now under the guidance of the Apache Software Foundation, the project gets more contributions than any other Apache software project. The Spark project was started in 2008 at the University of California, Berkeley's AMPLab (the AMP stands for Algorithms, Machine and People). Other Hadoop distributors, notably Hortonworks and MapR, also offer Spark in their distributions. Hadoop distributor Cloudera, which also includes Spark in its releases, has about 60 enterprise customers using Spark in some form or another, according to Monash. "Things that Hadoop MapReduce was pretty good at, Spark is potentially better at," Monash said.Īnother early adopter of Spark has been music streaming service Spotify, which uses the technology to generate playlists of music based on the user's specific tastes based on a set of machine learning algorithms.Įven Hadoop users are getting the message. In contrast, Spark was designed to tackle more complex queries involving techniques of machine learning and predictive modeling, among others. Hadoop's default analysis engine, MapReduce, is chiefly capable of executing one kind of problem, involving the filtering and sorting of data across different servers (the "map" portion of the job) and the summarizing of the results (the "reduce" side of the problem). Spark also offers a richer palate of ways to analyze data, Monash said. Spark's data processing speed is important, because while the amount of data we collect is growing rapidly, the advancement of computer processing power is tapering off. For instance, Spark could be used to help digital advertisers decide what ad to serve to users based on their last few clicks, rather than on what sites they clicked on a few days or weeks prior. They don't want to wait a day for an answer," Ghodsi said.
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The data can come from many sources and can be updated as new data comes in.
#Big data analysis with apache spark series
ClearStory Data offers a new business intelligence service that allows teams to assemble a series of data visualizations into a narrative, as if they were a PowerPoint presentation. "We've built our intellectual property around Spark," explained ClearStory Data CEO and co-founder Sharmila Shahani-Mulligan. Initially, real-time processing may not seem like a big distinction, however, such capabilities have been used to create entirely new lines of businesses. In the annual Daytona Gray Sort Challenge, which benchmarks the speed of data analysis systems, Spark easily trumped Hadoop MapReduce, and was able to sort through 100 terabytes of records within 23 minutes It took Hadoop over three times as long to execute the same task, about 72 minutes. Spark, however, goes beyond what Hadoop can easily do, in that it can analyze streaming data as it is coming off the wire.Īs such, it can serve as a faster replacement to the Hadoop MapReduce framework for data analysis. Also like Hadoop, Spark can work on unstructured data, such as event logs, that hasn't been formatted into database tables.
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Like Hadoop, Spark can be used to examine data sets that are too large to fit into a traditional data warehouse or a relational database. Spark is an engine for analyzing data stored across a cluster of computers.