Enkitec E4 Barcelona : SQL and Data Integration Futures on Hadoop :

Presentation from BIWA Summit 2016 on using Oracle Big Data Graph and Spatial to analyse the social network around rittmanmead.com

As presented at UKOUG Tech15 - covers deploying full OBIEE systems including DW and ETL to Oracle Public Cloud

Mark Rittman presented at Big Data World in London in March 2017 on data integration and data warehousing for cloud, big data, and IoT. He discussed the history of data warehousing and how it has evolved from traditional RDBMS implementations to embrace big data technologies like Hadoop. He described how cloud data warehouse offerings from Google BigQuery and Amazon Redshift combine the scalability of big data with the structure of data warehousing. Rittman also covered new approaches to ETL using data pipelines, schema discovery using machine learning, emerging open-source BI tools, and his current work in these areas.

The document discusses a company's migration from their in-house computation engine to Apache Spark. It describes five key issues encountered during the migration process: 1) difficulty adapting to Spark's low-level RDD API, 2) limitations of DataSource predicates, 3) incomplete Spark SQL functionality, 4) performance issues with round trips between Spark and other systems, and 5) OutOfMemory errors due to large result sizes. Lessons learned include being aware of new Spark features and data formats, and designing architectures and data structures to minimize data movement between systems.

Big Data integration is an excellent feature in the Oracle Data Integration product suite (Oracle Data Integrator, GoldenGate, & Enterprise Data Quality). But not all analytics require big data technologies, such as labor cost, revenue, or expense reporting. Ralph Kimball, an original architect of the dimensional model in data warehousing, spent much of his career working to build an enterprise data warehouse methodology that can meet these reporting needs. His book, "The Data Warehouse ETL Toolkit", is a guide for many ETL developers. This session will walk you through his ETL Subsystem categories; Extracting, Cleaning & Conforming, Delivering, and Managing, describing how the Oracle Data Integration products are perfectly suited for the Kimball approach. Presented at Collaborate16 in Las Vegas.

Build a simple data lake on AWS using a combination of services, including AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, Amazon Relational Database Service (Amazon RDS), and Amazon S3. Link to the blog post and video: https://garystafford.medium.com/building-a-simple-data-lake-on-aws-df21ca092e32

The document discusses building data lakes on AWS. It describes how data lakes extend the traditional data warehouse approach by allowing storage of both structured and unstructured data at massive scales. Amazon S3 provides durable, available, scalable, and easy-to-use storage for the data lake. AWS Glue crawls data to create a data catalog and can automate ETL processes. Amazon Athena and Amazon EMR enable interactive analysis and big data processing through SQL and Spark. The data lake architecture on AWS supports a variety of analytical use cases.

Doug Bateman, a principal data engineering instructor at Databricks, presented on how to build a Lakehouse architecture. He began by introducing himself and his background. He then discussed the goals of describing key Lakehouse features, explaining how Delta Lake enables it, and developing a sample Lakehouse using Databricks. The key aspects of a Lakehouse are that it supports diverse data types and workloads while enabling using BI tools directly on source data. Delta Lake provides reliability, consistency, and performance through its ACID transactions, automatic file consolidation, and integration with Spark. Bateman concluded with a demo of creating a Lakehouse.

knowIT is a collaborative semantic wiki used by Johnson & Johnson to map their IT systems, applications, servers and stakeholders. It aims to capture knowledge about these informatics systems, their relationships and components to answer questions, facilitate knowledge sharing and enable self-service. The wiki uses Semantic MediaWiki and has grown to include systems portfolio management, configuration management and other features to increase IT systems knowledge across the organization.

Data Con LA 2020 Description In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake. Speaker Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning

The document discusses machine learning and artificial intelligence applications inside and outside of Snowflake's cloud data warehouse. It provides an overview of Snowflake and its architecture. It then discusses how machine learning can be implemented directly in the database using SQL, user-defined functions, and stored procedures. However, it notes that pure coding is not suitable for all users and that automated machine learning outside the database may be preferable to enable more business analysts and power users. It provides an example of using Amazon Forecast for time series forecasting and integrating it with Snowflake.

INFORMATICA ONLINE TRAINING BY QUONTRA SOLUTIONS WITH PLACEMENT ASSISTANCE We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by Quontra Solutions are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews. Emphasis is given on important topics which are essential and mostly used in real time projects. Quontra Solutions is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills. Training Features at Quontra Solutions: We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest. Course content: • Basics of data warehousing concepts • Power center components • Informatica concepts and overview • Sources • Targets • Transformations • Advanced Informatica concepts Please Visit us for the Demo Classes, we have regular batches and weekend batches. QUONTRASOLUTIONS 204-226 Imperial Drive,Rayners Lane, Harrow-HA2 7HH Phone : +44 (0)20 3734 1498 / 99 Email: info@quontrasolutions.co.uk

If you've invested in OBIEE and want to start exploring the use of Big Data technology, this presentation talks about how and why you might want to use OBIEE as the common visualization layer across both.

This session will cover building the modern Data Warehouse by migration from the traditional DW platform into the cloud, using Amazon Redshift and Cloud ETL Matillion in order to provide Self-Service BI for the business audience. This topic will cover the technical migration path of DW with PL/SQL ETL to the Amazon Redshift via Matillion ETL, with a detailed comparison of modern ETL tools. Moreover, this talk will be focusing on working backward through the process, i.e. starting from the business audience and their needs that drive changes in the old DW. Finally, this talk will cover the idea of self-service BI, and the author will share a step-by-step plan for building an efficient self-service environment using modern BI platform Tableau.

The document discusses Talend's big data solutions and sandbox. It introduces Rajan Kanitkar as a senior solutions engineer at Talend with 15 years of experience in data integration. It then summarizes Talend's big data platform and ecosystem including Hadoop, MapReduce, HDFS, Hive and more. The rest of the document describes Talend's sandbox, which provides a pre-configured virtual image with Hadoop distributions, Talend software, and data scenarios to demonstrate ingesting, transforming and delivering big data.

As a follow-on to the presentation "Building an Effective Data Warehouse Architecture", this presentation will explain exactly what Big Data is and its benefits, including use cases. We will discuss how Hadoop, the cloud and massively parallel processing (MPP) is changing the way data warehouses are being built. We will talk about hybrid architectures that combine on-premise data with data in the cloud as well as relational data and non-relational (unstructured) data. We will look at the benefits of MPP over SMP and how to integrate data from Internet of Things (IoT) devices. You will learn what a modern data warehouse should look like and how the role of a Data Lake and Hadoop fit in. In the end you will have guidance on the best solution for your data warehouse going forward.

AI&BigData Conference 2017 Eugene Polonichko "Azure Data Lake: what is it? why is it? where is it?"

The document discusses Oracle's new approach to business analytics and visualization. It notes that traditional corporate BI systems are viewed as inflexible and analytics are only for a privileged few. However, it argues there is still hope as analytics can provide a 10x ROI. The new approach involves visual analytics embedded in every Oracle solution across mobile, cloud, on-premises and big data to provide a single, integrated platform that allows business users to easily access, blend and scale insights from various data sources.

The Yorkshire Terrier, commonly known as the Yorkie, is descended from the Clydesdale Terrier and Paisley Terrier, which are now extinct. It also has ancestry from the Skye Terrier and Airdale Terrier. Yorkies are small dogs with black fur and a brown underside. They are intelligent but can be snappy. This document provides a list of supplies needed to care for a Yorkie and background on the breed. It encourages adopting from rescues, sharing details about a Yorkie named Magellan available for adoption.

TUGAS AKHIR PPT

El documento habla sobre Julian Beever, un artista que ha estado creando ilusiones ópticas en aceras de Europa, Estados Unidos y Australia durante los últimos 10 años. Sus creaciones se ven tan realistas que parecen tridimensionales aunque en realidad son dibujos en el pavimento, lo que sorprende a quienes las ven.

The document discusses important conditions for starting new businesses and defines economic terms. The three most important conditions are low taxes, skilled staff, and low interest rates. It also provides definitions for various economic terms like interest rate, exchange rate, inflation rate, labor force, tax incentives, and balance of trade.

This infographic document provides information on what infographics are and why they are effective communication tools. It discusses that infographics tell visual stories using images and graphics to engage audiences better than plain text. Effective infographics are simple, visually pleasing, and help explain complex topics. They improve comprehension and retention of information by leveraging human visual processing abilities.

Ashutosh Rubber Pvt Ltd is an Indian manufacturer and exporter of rubber products established in 2007. It produces over 9000 rubber products for industries like automotive, generators, and oil. The company exports to countries including Australia, Singapore, and Denmark. It has 26-50 employees and certifications for quality standards.

This document discusses building a city as a hands-on project to develop fine motor, visual motor, problem solving, imagination, and creativity skills in children. The project uses construction materials to build a cityscape that allows children to use their hands to physically construct buildings and structures while also using their visual skills to envision what they are creating, their problem solving to determine how pieces fit together, and their imagination to design the city layout.

Dokumen tersebut membahas mengenai kejuruteraan genetik yang merupakan teknik modifikasi genetika untuk meningkatkan kualitas tanaman dan hewan. Teknologi ini telah banyak digunakan dalam industri pertanian, peternakan, dan kedokteran untuk menghasilkan produk yang lebih baik dan bermanfaat bagi masyarakat. Contoh penerapannya adalah tanaman yang tahan penyakit dan cuaca ekstrem, hewan ternak

Dokumen ini membahas tentang sumberdaya alam, sumberdaya manusia, dan klasifikasi sumberdaya alam. Sumberdaya alam dibedakan menjadi sumberdaya terbarukan dan tidak terbarukan, sedangkan sumberdaya manusia diukur berdasarkan jumlah penduduk yang bekerja di sektor pertanian.

Salesforce もくもく会 #10

Teks tersebut berisi soalan-soalan latihan semantik dan peribahasa. Teks tersebut memberikan contoh-contoh kata dan ungkapan yang memiliki makna ganda atau kiasan, serta meminta pembaca untuk mengidentifikasi makna sebenarnya atau ungkapan yang tepat. Teks tersebut juga memberikan jawaban untuk soalan-soalan tersebut.

CFMS provides facility management services including housekeeping, maintenance, and production support staff out of its headquarters in Pune and offices in Ahmedabad. It has a highly trained team and uses quality equipment and chemicals. CFMS is committed to understanding client needs and creating customized service programs. It has an in-house training center to deploy trained staff from day one and implements consistent management systems. Some of CFMS' clients include restaurants, hotels, and automation companies in Pune.

Salesforce Developers Life

Este documento habla sobre una persona extraordinaria que ayudó a Lorenzo a superar sus miedos y sacar la cabeza de su cazo. Esta persona le mostró sus puntos fuertes y le ayudó a expresar sus temores. Ahora Lorenzo puede jugar con los demás y los demás ven sus cualidades, aunque él sigue siendo el mismo.

Mark Rittman, CTO of Rittman Mead, gave a keynote presentation on big data for Oracle developers and DBAs with a focus on Apache Spark, real-time analytics, and predictive analytics. He discussed how Hadoop can provide flexible, cheap storage for logs, feeds, and social data. He also explained several Hadoop processing frameworks like Apache Spark, Apache Tez, Cloudera Impala, and Apache Drill that provide faster alternatives to traditional MapReduce processing.

Hadoop and NoSQL platforms initially focused on Java developers and slow but massively-scalable MapReduce jobs as an alternative to high-end but limited-scale analytics RDBMS engines. Apache Hive opened-up Hadoop to non-programmers by adding a SQL query engine and relational-style metadata layered over raw HDFS storage, and since then open-source initiatives such as Hive Stinger, Cloudera Impala and Apache Drill along with proprietary solutions from closed-source vendors have extended SQL-on-Hadoop’s capabilities into areas such as low-latency ad-hoc queries, ACID-compliant transactions and schema-less data discovery – at massive scale and with compelling economics. In this session we’ll focus on technical foundations around SQL-on-Hadoop, first reviewing the basic platform Apache Hive provides and then looking in more detail at how ad-hoc querying, ACID-compliant transactions and data discovery engines work along with more specialised underlying storage that each now work best with – and we’ll take a look to the future to see how SQL querying, data integration and analytics are likely to come together in the next five years to make Hadoop the default platform running mixed old-world/new-world analytics workloads.

Most DBAs are aware something interesting is going on with big data and the Hadoop product ecosystem that underpins it, but aren't so clear about what each component in the stack does, what problem each part solves and why those problems couldn't be solved using the old approach. We'll look at where it's all going with the advent of Spark and machine learning, what's happening with ETL, metadata and analytics on this platform ... why IaaS and datawarehousing-as-a-service will have such a big impact, sooner than you think

This document discusses Microsoft's efforts to make big data technologies like Hadoop more accessible through its products. It describes Hadoop, MapReduce, HDFS, and other big data concepts. It then outlines Microsoft's project to create a Hadoop distribution that runs on Windows Server and Windows Azure, including building an ODBC driver to allow tools like Excel to query Hadoop. This will help bring big data to more business users and integrate it with Microsoft's existing BI technologies.

A primer on Big Data, Hadoop and Microsoft's implementation of Hadoop for Windows Server and Windows Azure.

This document summarizes a meetup about Big Data and SQL on Hadoop. The meetup included discussions on what Hadoop is, why SQL on Hadoop is useful, what Hive is, and introduced IBM's BigInsights software for running SQL on Hadoop with improved performance over other solutions. Key topics included HDFS file storage, MapReduce processing, Hive tables and metadata storage, and how BigInsights provides a massively parallel SQL engine instead of relying on MapReduce.

This document discusses big data analytics platforms and techniques. It describes various open-source projects like Hadoop, Spark, and Mahout that can perform analytics on large datasets. It also discusses commercial analytics platforms from vendors like SAS, Alpine, and Revolution Analytics. Spark is highlighted as gaining rapid adoption for its speed and expanding machine learning capabilities. Key questions are raised about which open-source projects and commercial offerings will emerge as leaders in their categories.

Presentation given at the Danish BI Meetup in September 2016, on the future of BI and how Hadoop, and Big Data, will revolutionise the industry.

The document discusses cloud computing systems and MapReduce. It provides background on MapReduce, describing how it works and how it was inspired by functional programming concepts like map and reduce. It also discusses some limitations of MapReduce, noting that it is not designed for general-purpose parallel processing and can be inefficient for certain types of workloads. Alternative approaches like MRlite and DCell are proposed to provide more flexible and efficient distributed processing frameworks.

This document provides an overview of big data processing techniques including batch processing using MapReduce and Hive, iterative batch processing using Spark, stream processing using Apache Storm, and OLAP over big data using Dremel and Druid. It discusses techniques such as MapReduce, Hive, Spark RDDs, and Storm tuples for processing large datasets and compares small versus big data approaches. Example usages and technologies for different processing types are also outlined.

This document provides an agenda and overview for a conference session on Big Data and NoSQL for database and BI professionals held from April 10-12 in Chicago, IL. The session will include an overview of big data and NoSQL technologies, then deeper dives into Hadoop, NoSQL databases like HBase, and tools like Hive, Pig, and Sqoop. There will also be demos of technologies like HDInsight, Elastic MapReduce, Impala, and running MapReduce jobs.