Implementing Hadoop on a single cluster

This document discusses anomaly detection techniques. It introduces anomaly detection as finding patterns in data that do not conform to expected behavior. It covers applications like intrusion detection, fraud detection, and industrial damage detection. The document outlines challenges like defining normal behavior and dealing with noise. It differentiates between types of anomalies and detection methods, including supervised, semi-supervised and unsupervised techniques. Finally, it categorizes anomaly detection techniques as classification, nearest neighbor, clustering, spectral, information theoretic and statistical approaches.

MapReduce and Hadoop

MapReduce and Hadoop provide a framework for processing vast amounts of data across clusters of computers. It allows distributed processing of large datasets in a parallel and fault-tolerant manner. The key components are HDFS for storage, and MapReduce for distributed processing. HDFS stores data reliably across commodity hardware, while MapReduce breaks jobs into map and reduce tasks that can run in parallel across a cluster.

Anomaly Detection

This document discusses anomaly detection, which involves finding patterns in data that do not conform to expected behavior. It covers applications such as intrusion detection, fraud detection, and industrial damage detection. It also discusses challenges in anomaly detection like defining normal behavior and dealing with noise. Finally, it outlines different techniques for anomaly detection including classification, nearest neighbors, clustering, spectral methods, and statistical approaches.

Hadoop Overview kdd2011

Milind Bhandarkar

Challenges of Implementing an Advanced SQL Engine on Hadoop

Big SQL 3.0 is IBM's SQL engine for Hadoop that addresses challenges of building a first class SQL engine on Hadoop. It uses a modern MPP shared-nothing architecture and is architected from the ground up for low latency and high throughput. Key challenges included data placement on Hadoop, reading and writing Hadoop file formats, query optimization with limited statistics, and resource management with a shared Hadoop cluster. The architecture utilizes existing SQL query rewrite and optimization capabilities while introducing new capabilities for statistics, constraints, and pushdown to Hadoop file formats and data sources.

Hadoop Operations: Starting Out Small / So Your Cluster Isn't Yahoo-sized (yet)

Michael Arnold

Hadoop Summit 2012 - Deployment and Operations track Everyone hears about large clusters with thousands of machines and petabytes of storage yet not everyone starts their first Hadoop deployment with dozens of cabinets of equipment. What do you do when you don`t have quite as large of a deployment? What decisions should you make now and which should you postpone for later? This session is for SysAdmins that have not yet or just recently jumped into the Hadoop fray. You will be presented with the knowledge gained from two years of operational experience at a (currently) small Hadoop site. We will discuss things that are initially important for a small (10-100 node) cluster and what happens when you outgrow your first deployment.

Data Mining and Recommendation Systems

This document discusses data mining techniques and recommendation systems. It describes common data mining techniques like classification, clustering, regression, association rule mining and outlier analysis. It also discusses the knowledge discovery process and applications of data mining. The document then covers recommendation systems, describing content-based, collaborative filtering and hybrid recommendation approaches. It provides examples of these systems.

Data-Ed Webinar: A Framework for Implementing NoSQL, Hadoop

DATAVERSITY

Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case. Takeaways: A Framework for evaluating Big Data techniques Deciding on a Big Data platform – How do you know which one is a good fit for you? The means by which big data techniques can complement existing data management practices The prototyping nature of practicing big data techniques The distinct ways in which utilizing Big Data can generate business value

Modeling with Hadoop kdd2011

Milind Bhandarkar

This document discusses modeling algorithms using the MapReduce framework. It outlines types of learning that can be done in MapReduce, including parallel training of models, ensemble methods, and distributed algorithms that fit the statistical query model (SQM). Specific algorithms that can be implemented in MapReduce are discussed, such as linear regression, naive Bayes, logistic regression, and decision trees. The document provides examples of how these algorithms can be formulated and computed in a MapReduce paradigm by distributing computations across mappers and reducers.

Monitor PowerKVM using Ganglia, Nagios

Pradeep Kumar

This document discusses monitoring PowerKVM nodes using the Ganglia and Nagios monitoring systems. It provides an overview of Ganglia and how it can be used to monitor metrics like CPU, memory, disk, and network usage on PowerKVM nodes and their virtual machines. It also discusses how Nagios can monitor PowerKVM hosts and services, how to add PowerKVM nodes to the Nagios server, and examples of host states and performance monitoring that Nagios provides.

Hadoop installation, Configuration, and Mapreduce program

Praveen Kumar Donta

Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...

This document discusses using Hadoop and the Hortonworks Data Platform (HDP) for big data applications. It outlines how HDP can help organizations optimize their existing data warehouse, lower storage costs, unlock new applications from new data sources, and achieve an enterprise data lake architecture. The document also discusses how Talend's data integration platform can be used with HDP to easily develop batch, real-time, and interactive data integration jobs on Hadoop. Case studies show how companies have used Talend and HDP together to modernize their data architecture and product inventory and pricing forecasting.

Hadoop Integration into Data Warehousing Architectures

Humza Naseer

A Reference Architecture for ETL 2.0

More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.

Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals

Cloudera, Inc.

The enormous legacy of EDW experience and best practices can be adapted to the unique capabilities of the Hadoop environment. In this webinar, in a point-counterpoint format, Dr. Kimball will describe standard data warehouse best practices including the identification of dimensions and facts, managing primary keys, and handling slowly changing dimensions (SCDs) and conformed dimensions. Eli Collins, Chief Technologist at Cloudera, will describe how each of these practices actually can be implemented in Hadoop.

Implementing a Data Lake with Enterprise Grade Data Governance

Hadoop provides a powerful platform for data science and analytics, where data engineers and data scientists can leverage myriad data from external and internal data sources to uncover new insight. Such power is also presenting a few new challenges. On the one hand, the business wants more and more self-service, and on the other hand IT is trying to keep up with the demand for data, while maintaining architecture and data governance standards. In this webinar, Andrew Ahn, Data Governance Initiative Product Manager at Hortonworks, will address the gaps and offer best practices in providing end-to-end data governance in HDP. Andrew Ahn will be followed by Oliver Claude of Waterline Data, who will share a case study of how Waterline Data Inventory works with HDP in the Modern Data Architecture to automate the discovery of business and compliance metadata, data lineage, as well as data quality metrics.

Hadoop and Enterprise Data Warehouse

This document discusses how Hadoop can be used in data warehousing and analytics. It begins with an overview of data warehousing and analytical databases. It then describes how organizations traditionally separate transactional and analytical systems and use extract, transform, load processes to move data between them. The document proposes using Hadoop as an alternative to traditional data warehousing architectures by using it for extraction, transformation, loading, and even serving analytical queries.

Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...

How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services? Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business. Join Hortonworks and Informatica as we discuss: - What is a data lake? - The modern data architecture for a data lake - How Hadoop fits into the modern data architecture - Innovative use-cases for a data lake

Hadoop and Your Data Warehouse

Caserta

This document discusses how Hadoop can be used to power a data lake and enhance traditional data warehousing approaches. It proposes a holistic data strategy with multiple layers: a landing area to store raw source data, a data lake to enrich and integrate data with light governance, a data science workspace for experimenting with new data, and a big data warehouse at the top level with fully governed and trusted data. Hadoop provides distributed storage and processing capabilities to support these layers. The document advocates a "polygot" approach, using the right tools like Hadoop, relational databases, and cloud platforms depending on the specific workload and data type.

Large scale ETL with Hadoop

OReillyStrata

Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem – HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on – it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.

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Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...

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