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Distributed SQL Query Engine for Big Data

Distributed SQL Query Engine for Big Data
Related:  Hadoop

Index - Apache ZooKeeper Skip to end of metadataGo to start of metadata ZooKeeper: Because coordinating distributed systems is a Zoo ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. ZooKeeper aims at distilling the essence of these different services into a very simple interface to a centralized coordination service. We have Java and C interfaces to Zoo Keeper for the applications themselves.

Hadapt Schemaless SQL – Webinar Questions Answered A couple of weeks ago we hosted a webinar on Schemaless SQL and the ability to query all data types through one familiar interface. We received some great questions and far too many to answer at the end of the webinar, so within this post we’ll address those remaining. If you were unavailable to attend the webinar, a replay is available here; additionally, reviewing a primer on Schemaless SQL™ and Multi-Structured Tables™ may be worthwhile for additional context on the questions below: How is the inverted index stored? The inverted index is stored alongside the re-serialized JSON data. Is this solution one-dimensional? This solution is not one-dimensional. Are the reverse indexes “bloom filters”? The inverted indexes on the virtual columns are not bloom filters. How do you represent nested structure in JSON in term of these attributes? Nested JSON can be stored in multiple ways in the Hadapt platform. There is no columnar authentication in Hadapt databases. Absolutely.

Zeppelin Apache Hadoop 2.5.0 - Hadoop in Secure Mode Common Configurations In order to turn on RPC authentication in hadoop, set the value of hadoop.security.authentication property to "kerberos", and set security related settings listed below appropriately. The following properties should be in the core-site.xml of all the nodes in the cluster. Configuration for WebAppProxy The WebAppProxy provides a proxy between the web applications exported by an application and an end user. LinuxContainerExecutor A ContainerExecutor used by YARN framework which define how any container launched and controlled. The following are the available in Hadoop YARN: To build the LinuxContainerExecutor executable run: $ mvn package -Dcontainer-executor.conf.dir=/etc/hadoop/ The path passed in -Dcontainer-executor.conf.dir should be the path on the cluster nodes where a configuration file for the setuid executable should be located. conf/container-executor.cfg The executable requires following configuration items to be present in the conf/container-executor.cfg file.

The Top of the Big Data Stack Database Applications - EnterpriseStorageForum In May, Henry kicked off a collaborative effort to examine some of the details behind the Big Data push and what they really mean. This article is the third in our muiltipart series and the second of three to take a high-level examination of Big Data from the top of the stack -- that is, the applications. Introduction Henry and I have undertaken the task of examining Big Data and what it really means. Henry kicked off the series with a great introduction, including what I consider to be the best definition for Big Data. This definition is so appropriate because the adjective "Big" can mean many things to many fields of interest. Henry and I have chosen to tackle the discussion by coming from two different directions. Starting at the top isn't easy, and my original article became rather lengthy, so we broke it into three parts. Wide Column Store/Column FamiliesDocument StoreKey Value/Tuple StoreGraph DatabasesMultimodel DatabasesObject DatabasesMultivalue databasesRDF databases

(Optional) Create Bootstrap Actions to Install Additional Software - Amazon Elastic MapReduce You can use a bootstrap action to install additional software and to change the configuration of applications on the cluster. Bootstrap actions are scripts that are run on the cluster nodes when Amazon EMR launches the cluster. They run before Hadoop starts and before the node begins processing data. You can create custom bootstrap actions, or use predefined bootstrap actions provided by Amazon EMR. A common use of bootstrap actions is to change the Hadoop configuration settings. Bootstrap actions execute as the Hadoop user by default. All Amazon EMR management interfaces support bootstrap actions. From the Amazon EMR console, you can optionally specify a bootstrap action while creating a cluster. When you use the CLI, you can pass references to bootstrap action scripts to Amazon EMR by adding the --bootstrap-action parameter when you create the cluster using the create-cluster command. --bootstrap-action Path= Use Predefined Bootstrap Actions Note <?

Comparing Pattern Mining on a Billion Records with HP Vertica and Hadoop Pattern mining can help analysts discover hidden structures in data. Pattern mining has many applications—from retail and marketing to security management. For example, from a supermarket data set, you may be able to predict whether customers who buy Lay’s potato chips are likely to buy a certain brand of beer. A pattern mining algorithm Frequent patterns are items that occur often in a data set. Instead of describing FP-growth in detail, we list the main steps from a practitioner’s perspective. Create transactions of itemsCount occurrence of item setsSort item sets according to their occurrenceRemove infrequent itemsScan DB and build FP-treeRecursively grow frequent item sets Let’s use an example to illustrate these steps. Parallel pattern mining on the HP Vertica Analytics Platform Despite the efficiency of the FP-Growth algorithm, single-threaded sequential version of FP-Growth can take very long on large data sets. The real test: a billion records, and, of course, Hadoop

A Year Makes a Big Difference for Big Data Analytics Users of big data analytics are finally going public. At the Hadoop Summit last June, many vendors were still speaking of a large retailer or a big bank as users but could not publically disclose their partnerships. Companies experimenting with big data analytics felt that their proof of concept was so innovative that once it moved into production, it would yield a competitive advantage to the early mover. Now many companies are speaking openly about what they have been up to in their business laboratories. I look forward to attending the 2013 Hadoop Summit in San Jose to see how much things have changed in just a single year for Hadoop centered big data analytics. Our benchmark research into operational intelligence, which I argue is another name for real-time big data analytics, shows diversity in big data analytics use cases by industry. The retail industry, driven by market forces and facing discontinuous change, is adopting big data analytics out of competitive necessity. Regards,

awslabs/emr-bootstrap-actions Apache Spark™ - Lightning-Fast Cluster Computing 4 Barriers to Big Data Analytics in Healthcare Organizations 84% of CIOs and other C-Suite health care executives believe that the application of big data analytics in healthcare organizations is a significant challenge , according to a survey from the eHealth Initiative and the College of Health Information Management Executives . Key stakeholders from over 102 healthcare organizations participated in the survey conducted over a four week period from May 30 to June 28, 2013 examined the attitudes toward data use, trends in business use cases for data and analytics, the technological solutions employed by organizations, and associated challenges and barriers. To adapt the growing volume of electronic data, healthcare organizations are increasing their focus on building a scalable plan to leverage data and predictive analytics that meets their organization’s strategic plans. Despite the growing focus on big data and analytics, the survey identified four major barriers: Other survey findings include: Click here for the full survey findings

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