070416 Egu Vienna Husar

2016 is the year of the data lake. As you consider adopting an enterprise data lake strategy to manage more dynamic, poly-structured data, your data integration strategy must also evolve to handle new requirements. Thinking you can simply hire more developers to write code or rely on your legacy rows-and-columns centric tools is a recipe to sink in a data swamp instead of swimming in a data lake. In this presentation, you'll learn about eight enterprise data management requirements that must be addressed in order to get maximum value from your big data technology investments. To learn more, visit: https://www.snaplogic.com/big-data

quick guide to refresh your spark skills - especially used while preparing for interviews and getting a overview of spark-sql,core & streaming

The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.

It Tells hoe ETL does with map reduce techniques and architecure of ETL with HDFS(Hadoop Distributed File System)

Ever spotted some great looking software only to discover you can’t get it, it doesn’t work, there is no documentation to help fix it and the developers don’t have the time or incentive to help? Ever produced some software that you want to be widely used or have folks contribute? What’s the sustainability of that key platform/library/tool /database your lab uses day in and day out? Are you helping the providers? The same issues stand for Data (or as we now say “FAIR” Findable, Accessible, Interoperable, Reusable Data) and its metadata. Is anyone looking out for Europe’s data services– the datasets and analysis systems you use and you make – the standards they use and the curators and developers who make them? Or is FAIR just a FAIRy story? I’ll tell how two organisations with quite different structures and approaches - the UK’s Software Sustainability Institute and the ELIXIR European Research Infrastructure for Life Science Data – are working for the common goal of better software, better service, and better research. https://www.rothamsted.ac.uk/events/14th-international-symposium-integrative-bioinformatics

The document proposes a four-layer model for providing cloud-based archiving services that enables long-term digital preservation. The model builds on the OAIS reference model and adds a preservation layer to capture preservation metadata and package digital objects early in their lifecycle. A case study on archiving challenges in the Japanese government demonstrates how the model could integrate systems and provide automated preservation functionality across agencies using a shared cloud platform and services.

The document discusses the need for a new open source database management system called SciDB to address the challenges of storing and analyzing extremely large scientific datasets. SciDB is being designed to handle petabyte-scale multidimensional array data with native support for features important to science like provenance tracking, uncertainty handling, and integration with statistical tools. An international partnership involving scientists, database experts, and a nonprofit company is developing SciDB with initial funding and use cases coming from astronomy, industry, genomics and other domains.

Watch here: https://bit.ly/36tEThx The current data landscape is fragmented, not just in location but also in terms of shape and processing paradigms. Cloud has become a key component of modern architecture design. Data lakes, IoT, NoSQL, SaaS, etc. coexist with relational databases to fuel the needs of modern analytics, ML and AI. Exploring and understanding the data available within your organization is a time-consuming task. Dealing with bureaucracy, different languages and protocols, and the definition of ingestion pipelines to load that data into your data lake can be complex. And all of this without even knowing if that data will be useful at all. Attend this session to learn: - How dynamic data challenges and the speed of change requires a new approach to data architecture – one that is real-time, agile and doesn’t rely on physical data movement. - Learn how logical data architecture can enable organizations to transition data faster to the cloud with zero downtime and ultimately deliver faster time to insight. - Explore how data as a service and other API management capabilities is a must in a hybrid cloud environment.

This is a brief technology introduction to Oracle Stream Analytics, and how to use the platform to develop streaming data pipelines that support a wide variety of industry use cases

Keynote at Scale By The Bay 2020. Cloud service developers need to handle massive scale workloads from thousands of customers with no downtime or regressions. In this talk, I’ll present our experience building a very large-scale cloud service at Databricks, which provides a data and ML platform service used by many of the largest enterprises in the world. Databricks manages millions of cloud VMs that process exabytes of data per day for interactive, streaming and batch production applications. This means that our control plane has to handle a wide range of workload patterns and cloud issues such as outages. We will describe how we built our control plane for Databricks using Scala services and open source infrastructure such as Kubernetes, Envoy, and Prometheus, and various design patterns and engineering processes that we learned along the way. In addition, I’ll describe how we have adapted data analytics systems themselves to improve reliability and manageability in the cloud, such as creating an ACID storage system that is as reliable as the underlying cloud object store (Delta Lake) and adding autoscaling and auto-shutdown features for Apache Spark.

Lucile Packard Children's Hospital at Stanford (LPCH) has a long history of using Cerner applications including PowerChart. The document discusses clinical and technical challenges LPCH has faced in using MPages, including how to store resources and implement AJAX functionality. It provides examples of current and future MPages projects at LPCH like integrating external databases into genviews and developing multi-patient dashboards for different units.

Integrated Library Systems Moving to the Cloud: Fair Skies or... Joseph R. Matthews, author and library consultant

This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.

In this session we will take a look at Azure Data Lake from an administrator's perspective. Do you know who has what access where? How much data is in your data lake? What about the accesses to the data lake, is everything running normally? In this session we will show you what possibilities the portal offers you to keep an eye on the Azure Data Lake. In addition, we will show you further scripts and tools to perform the corresponding tasks. Dive with us into the depths of your Data Lake.

This document discusses Oracle Data Integration solutions for tapping into big data reservoirs. It begins with an overview of Oracle Data Integration and how it can improve agility, reduce risk and costs. It then discusses Oracle's approach to comprehensive data integration and governance capabilities including real-time data movement, data transformation, data federation, and more. The document also provides examples of how Oracle Data Integration has been used by customers for big data use cases involving petabytes of data.

The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020. Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns. This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines. GoldenGate: https://www.oracle.com/middleware/tec... Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)

Started in 2004 (under ASTM Committee E13.15) the Analytical Information Markup Language (AnIML) is an XML based standard for capturing, sharing, viewing, and archiving analytical instrument data from any analytical technique. This paper discusses the AnIML standard in terms of philosophy, structure, usage, and the resources available to work with the standard. Examples will be given for different techniques as well as strategies for migration of legacy data. Finally, the current status of the standard and time frame for promulgation through ASTM will be reported.

The document describes an agile distributed air quality data system called DataFed. It discusses how DataFed facilitates access to heterogeneous air quality data from various autonomous providers through standard protocols and formats. DataFed transforms and homogenizes the data for uniform access and provides tools for collaborative analysis, reporting and dynamic delivery of information products to users.