This document provides an overview of MapR and compares its performance to Hadoop. It describes MapR's architecture including its use of containers to store and replicate data across nodes. Benchmark results show MapR outperforming Hadoop on streaming throughput, Terasort, HBase random reads, and small file operations. MapR provides a faster distributed file system compared to HDFS and its architecture has implications for integration with other technologies like search indexing and machine learning algorithms.
This document provides an overview of HBase and why NoSQL databases like HBase were developed. It discusses how relational databases do not scale horizontally well with large amounts of data. HBase was created by Google to address these scaling issues and was inspired by their BigTable database. The document explains the HBase data model with rows, columns, and versions. It describes how data is stored physically in HFiles and served from memory and disks. Basic operations like put, get, and scan are also covered.
Ted Dunning presents information on Drill and Spark SQL. Drill is a query engine that operates on batches of rows in a pipelined and optimistic manner, while Spark SQL provides SQL capabilities on top of Spark's RDD abstraction. The document discusses the key differences in their approaches to optimization, execution, and security. It also explores opportunities for unification by allowing Drill and Spark to work together on the same data.
Jim Scott, CHUG co-founder and Director, Enterprise Strategy and Architecture for MapR presents "Using Apache Drill". This presentation was given on August 13th, 2014 at the Nokia office in Chicago, IL.
Jim has held positions running Operations, Engineering, Architecture and QA teams. He has worked in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. His work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.
Apache Drill brings the power of standard ANSI:SQL 2003 to your desktop and your clusters. It is like AWK for Hadoop. Drill supports querying schemaless systems like HBase, Cassandra and MongoDB. Use standard JDBC and ODBC APIs to use Drill from your custom applications. Leveraging an efficient columnar storage format, an optimistic execution engine and a cache-conscious memory layout, Apache Drill is blazing fast. Coordination, query planning, optimization, scheduling, and execution are all distributed throughout nodes in a system to maximize parallelization. This presentation contains live demonstrations.
The video can be found here: http://vimeo.com/chug/using-apache-drill
Big data refers to large datasets that are difficult to process using traditional database management tools. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It provides reliable data storage with the Hadoop Distributed File System (HDFS) and high-performance parallel data processing using MapReduce. The Hadoop ecosystem includes components like HDFS, MapReduce, Hive, Pig, and HBase that provide distributed data storage, processing, querying and analysis capabilities at scale.
Spark Streaming is an extension of the core Spark API that enables continuous data stream processing. It is particularly useful when data needs to be processed in real-time. Carol McDonald, HBase Hadoop Instructor at MapR, will cover:
+ What is Spark Streaming and what is it used for?
+ How does Spark Streaming work?
+ Example code to read, process, and write the processed data
Drill into Drill – How Providing Flexibility and Performance is PossibleMapR Technologies
Learn how Drill achieves high performance with flexibility and ease of use. Includes: First read planning and statistics. Flexible code generation depending on workload. Code optimization and planning techniques. Dynamic schema subsets. Advanced memory use and moving between Java and C. Making a static typing appear dynamic through any-time and multi-phase planning.
This document provides an overview of Apache Spark Streaming. It discusses why Spark Streaming is useful for processing time series data in near-real time. It then explains key concepts of Spark Streaming like data sources, transformations, and output operations. Finally, it provides an example of using Spark Streaming to process sensor data in real-time and save results to HBase.
The document provides an overview of the state of the Apache HBase database project. It discusses the project goals of availability, stability, and scalability. It also summarizes the mature codebase, active development areas like region replicas and ProcedureV2, and the growing ecosystem of SQL interfaces and other Hadoop components integrated with HBase. Recent releases include 1.1.2 which improved scanners and introduced quotas and throttling, and the 1.0 release which adopted semantic versioning and added region replicas.
The document discusses new features in Apache Hadoop Common and HDFS for version 3.0. Key updates include upgrading the minimum Java version to Java 8, improving dependency management, adding a new Azure Data Lake Storage connector, and introducing erasure coding in HDFS to improve storage efficiency. Erasure coding in HDFS phase 1 allows for striping of small blocks and parallel writes/reads while trading off higher network usage compared to replication.
Efficient in situ processing of various storage types on apache tajoHyunsik Choi
The document discusses Apache Tajo, an open source data warehouse system that supports efficient in-situ processing of various storage types. It describes Tajo's architecture, how it supports different storage backends like HDFS, S3, HBase and data formats. The key points are:
1) Tajo provides a unified interface to integrate and process data from various storage systems and formats like HDFS, S3, HBase, in a single system.
2) It uses a pluggable storage and data format architecture with tablespaces to abstract different physical storage configurations.
3) Operations can be pushed down to underlying storages for optimization during query execution.
4) Current supported storages include HDFS, S
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
We share our slides about Apache Tez delivered as a lightening talk given at Warsaw Hadoop User Group http://www.meetup.com/warsaw-hug/events/218579675
The document discusses evolving HDFS to better support large scale deployments. It summarizes HDFS's strengths in scaling to large clusters and data sizes. However, scaling the large number of small files and blocks is challenging. The solution involves using partial namespaces to store only recently used metadata in memory, and block containers to group blocks together. This will generalize the storage layer to support different container types beyond HDFS blocks. Initial goals are to scale to billions of files and blocks per volume, with the ability to add more volumes for further scaling. The changes will also enable new use cases like block storage and caching data in cloud storage.
Flexible and Real-Time Stream Processing with Apache FlinkDataWorks Summit
This document provides an overview of stream processing with Apache Flink. It discusses the rise of stream processing and how it enables low-latency applications and real-time analysis. It then describes Flink's stream processing capabilities, including pipelining of data, fault tolerance through checkpointing and recovery, and integration with batch processing. The document also summarizes Flink's programming model, state management, and roadmap for further development.
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
Scale 12 x Efficient Multi-tenant Hadoop 2 Workloads with YarnDavid Kaiser
Hadoop is about so much more than batch processing. With the recent release of Hadoop 2, there have been significant changes to how a Hadoop cluster uses resources. YARN, the new resource management component, allows for a more efficient mix of workloads across hardware resources, and enables new applications and new processing paradigms such as stream-processing. This talk will discuss the new design and components of Hadoop 2, and examples of Modern Data Architectures that leverage Hadoop for maximum business efficiency.
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
Summary of recent progress on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
Apache Eagle is a distributed real-time monitoring and alerting engine for Hadoop created by eBay to address limitations of existing tools in handling large volumes of metrics and logs from Hadoop clusters. It provides data activity monitoring, job performance monitoring, and unified monitoring. Eagle detects anomalies using machine learning algorithms and notifies users through alerts. It has been deployed across multiple eBay clusters with over 10,000 nodes and processes hundreds of thousands of events per day.
Apache Drill [1] is a distributed system for interactive analysis of large-scale datasets, inspired by Google’s Dremel technology. It is a design goal to scale to 10,000 servers or more and to be able to process Petabytes of data and trillions of records in seconds. Since its inception in mid 2012, Apache Drill has gained widespread interest in the community. In this talk we focus on how Apache Drill enables interactive analysis and query at scale. First we walk through typical use cases and then delve into Drill's architecture, the data flow and query languages as well as data sources supported.
[1] http://incubator.apache.org/drill/
a Secure Public Cache for YARN Application ResourcesDataWorks Summit
This document discusses YARN's shared cache feature for application resources. It provides an overview of how YARN localizes resources for each application and containers. The shared cache aims to address inefficiencies in this process by caching identical resources on NodeManagers and sharing them between applications and containers. The design goals are for the shared cache to be scalable, secure, fault-tolerant and transparent. It works by having a shared cache client interface with a shared cache manager that maintains metadata and persisted resources. This can significantly reduce data transfer and localization costs for applications that reuse common resources.
The document discusses new directions for the Mahout machine learning library. It describes plans to remove unused and poorly maintained code in the next release to reduce bloat. It outlines work to improve the integration of core collections functionality and speed up k-nearest neighbor searches using techniques like projection search and fast k-means clustering algorithms. It also introduces a Pig Vector module to enable machine learning tasks like text vectorization and classification from Pig queries.
"The greater promise of Big Data lies not in doing old things in slightly new ways. Instead, it lies in doing new things that were previously not possible. One major class of new things is adding intelligence to large-scale systems. In this session I will present a survey of how machine learning can be applied to real-life situations without having to get a PhD in advanced mathematics. These systems can be built today from open source components to increase business revenues by understanding what customers need and want. I will provide real world examples of best practices and pitfalls in machine learning including practical ways to build maintainable, high performance systems." - Ted Dunning
Apache Drill is an open source project that provides interactive analysis of large-scale datasets through a SQL-like query language. It was inspired by Google's Dremel, which provides interactive querying of trillions of records. Drill uses a column-based storage and processing model and supports pluggable data formats and sources to provide a flexible, easy to use, and dependable system for interactive analysis of large datasets.
The document discusses new developments in the Apache Mahout machine learning library. Key points include: Mahout 0.8 will be released soon with many fixes and improvements, including a 10x speedup for QR decomposition; it may include new algorithms like Bayesian bandits and very fast online k-means clustering; and the author, Ted Dunning, is available for questions and discusses his work on generalized multi-armed bandits and fast streaming k-means clustering algorithms.
From the Hadoop Summit 2015 Session with Ted Dunning:
Just when we thought the last mile problem was solved, the Internet of Things is turning the last mile problem of the consumer internet into the first mile problem of the industrial internet. This inversion impacts every aspect of the design of networked applications. I will show how to use existing Hadoop ecosystem tools, such as Spark, Drill and others, to deal successfully with this inversion. I will present real examples of how data from things leads to real business benefits and describe real techniques for how these examples work.
The document provides an overview of MapR's distributed file system and improvements over traditional Hadoop implementations. Key points include:
- MapR partitions files into containers that are distributed across nodes, improving performance over HDFS which requires multiple copies.
- MapReduce on MapR is faster through direct RPC to receivers during shuffling, very wide merges, and leveraging the distributed file system.
- Benchmark results show MapR outperforming Hadoop on streaming workloads, TeraSort, HBase random reads, and small file creation rates.
- The container architecture is said to scale to exabyte-sized clusters with modest memory requirements for metadata caching.
The document discusses several scenarios for using Hadoop and MapR to address large-scale data and file management challenges. It describes how MapR improves upon Hadoop through innovations like containers, volumes, and transactional snapshots. It also provides examples of how MapR could be used to solve problems involving billions of files, global data distribution, real-time data processing, model deployment, video repositories, and backups.
Updated version of my talk about Hadoop 3.0 with the newest community updates.
Talk given at the codecentric Meetup Berlin on 31.08.2017 and on Data2Day Meetup on 28.09.2017 in Heidelberg.
MapR is an amazing new distributed filesystem modeled after Hadoop. It maintains API compatibility with Hadoop, but far exceeds it in performance, manageability, and more.
/* Ted's MapR meeting slides incorporated here */
This document summarizes improvements made to the read and write paths for HBase on HDFS. Major issues addressed were skewed disk usage due to large HDFS block sizes, high disk IOPS from small reads, and write outliers over 1 second. Solutions involved using inline checksums to reduce IOPS, syncing file ranges to avoid disk skew, locking pages during writeback to prevent outliers, and profiling to identify root causes. These changes helped optimize HBase performance on HDFS.
These slides are from a talk Ted Dunning gave at Lawrence Livermore Labs in 2011.
The talk gives an architectural outline of the MapR system and then discusses how this architecture facilitates large scale machine learning algorithms.
With Hadoop-3.0.0-alpha2 being released in January 2017, it's time to have a closer look at the features and fixes of Hadoop 3.0.
We will have a look at Core Hadoop, HDFS and YARN, and answer the emerging question whether Hadoop 3.0 will be an architectural revolution like Hadoop 2 was with YARN & Co. or will it be more of an evolution adapting to new use cases like IoT, Machine Learning and Deep Learning (TensorFlow)?
Shift into High Gear: Dramatically Improve Hadoop & NoSQL PerformanceMapR Technologies
MapR Architecture Presentation given at Strata + Hadoop World 2013 by MapR CTO & Co-Founder M.C. Srivas
Prior to co-founding MapR, Srivas ran one of the major search infrastructure teams at Google where GFS, BigTable and MapReduce were used extensively. He wanted to provide that powerful capability to everyone, and started MapR on his vision to build the next-generation platform for Big Data. His strategy was to evolve Hadoop and bring simplicity of use, extreme speed and complete reliability to Hadoop users everywhere, and make it seamlessly easy for enterprises to use this powerful new way to get deep insights. Srivas brings to MapR his experiences at Google, Spinnaker Networks, Transarc in building game-changing products that advance the state of the art.
These slides are from a recent talk I gave at Lawrence Livermore Labs.
The talk gives an architectural outline of the MapR system and then discusses how this architecture facilitates large scale machine learning algorithms.
The document discusses planning a MapR cluster, including hardware requirements and recommendations, operating system requirements, node configuration, service layout, and high availability cluster design considerations. The objectives are to understand MapR requirements, recommended hardware configurations for 50TB and 100TB clusters, how MapR services are arranged, and important factors for HA cluster design.
Netflix Open Source Meetup Season 4 Episode 2aspyker
In this episode, we will take a close look at 2 different approaches to high-throughput/low-latency data stores, developed by Netflix.
The first, EVCache, is a battle-tested distributed memcached-backed data store, optimized for the cloud. You will also hear about the road ahead for EVCache it evolves into an L1/L2 cache over RAM and SSDs.
The second, Dynomite, is a framework to make any non-distributed data-store, distributed. Netflix's first implementation of Dynomite is based on Redis.
Come learn about the products' features and hear from Thomson and Reuters, Diego Pacheco from Ilegra and other third party speakers, internal and external to Netflix, on how these products fit in their stack and roadmap.
- MapR makes it easy to deploy and run HBase in production by providing high availability, data protection, and disaster recovery features out of the box. This includes setting up hourly snapshots and cross-datacenter mirroring with just a few commands.
- With MapR, all HBase data should be stored in a single volume called "hbase.volume" for easy management. Snapshots are taken of this volume to enable consistent backups with no downtime. The snapshots can be mirrored to other clusters for disaster recovery.
- Memory usage on RegionServers is automatically tuned by MapR's Warden service based on configuration. The defaults generally work well but guidelines are provided for tuning in large deployments.
MapR M7: Providing an enterprise quality Apache HBase APImcsrivas
The document provides an overview of MapR M7, an integrated system for structured and unstructured data. M7 combines aspects of LSM trees and B-trees to provide faster reads and writes compared to Apache HBase. It achieves instant recovery from failures through its use of micro write-ahead logs and parallel region recovery. Benchmark results show MapR M7 providing 5-11x faster performance than HBase for common operations like reads, updates, and scans.
Based on "HBase, dances on the elephant back" presentation success I have prepared its update for JavaDay 2014 Kyiv. Again, it is about the product which revolutionary changes everything inside Hadoop infrastructure: Apache HBase. But here focus is shifted to integration and more advanced topics keeping presentation yet understandable for technology newcomers.
Scylla Summit 2016: Outbrain Case Study - Lowering Latency While Doing 20X IO...ScyllaDB
Outbrain is the world's largest content discovery program. Learn about their use case with Scylla where they lowered latency while doing 20X IOPS of Cassandra.
Clemson University's high performance computing storage includes the Palmetto cluster with 1972 nodes, 22928 cores, and 6 petabytes of storage. They are connecting research clusters across campus with 100 gigabit ethernet and expanding access to research data through WebDAV and the Innovation Platform. Performance testing of OrangeFS on Dell R720 servers with 10 gigabit ethernet showed read speeds up to 12 gigabytes per second and write speeds up to 8 gigabytes per second.
Cosco: An Efficient Facebook-Scale Shuffle ServiceDatabricks
Cosco is an efficient shuffle-as-a-service that powers Spark (and Hive) jobs at Facebook warehouse scale. It is implemented as a scalable, reliable and maintainable distributed system. Cosco is based on the idea of partial in-memory aggregation across a shared pool of distributed memory. This provides vastly improved efficiency in disk usage compared to Spark's built-in shuffle. Long term, we believe the Cosco architecture will be key to efficiently supporting jobs at ever larger scale. In this talk we'll take a deep dive into the Cosco architecture and describe how it's deployed at Facebook. We will then describe how it's integrated to run shuffle for Spark, and contrast it with Spark's built-in sort-based shuffle mechanism and SOS (presented at Spark+AI Summit 2018).
This document discusses new graphics APIs like DX12 and Vulkan that aim to provide lower overhead and more direct hardware access compared to earlier APIs. It covers topics like increased parallelism, explicit memory management using descriptor sets and pipelines, and best practices like batching draw calls and using multiple asynchronous queues. Overall, the new APIs allow more explicit control over GPU hardware for improved performance but require following optimization best practices around areas like parallelism, memory usage, and command batching.
How Data-Driven Approaches are Changing Your Data Management Strategies
Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.
ML Workshop 2: Machine Learning Model Comparison & EvaluationMapR Technologies
This document discusses machine learning model comparison and evaluation. It describes how the rendezvous architecture in MapR makes evaluation easier by collecting metrics on model performance and allowing direct comparison of models. It also discusses challenges like reject inferencing and the need to balance exploration of new models with exploitation of existing models. The document provides recommendations for change detection and analyzing latency distributions to better evaluate models over time.
Self-Service Data Science for Leveraging ML & AI on All of Your DataMapR Technologies
MapR has launched the MapR Data Science Refinery which leverages a scalable data science notebook with native platform access, superior out-of-the-box security, and access to global event streaming and a multi-model NoSQL database.
Enabling Real-Time Business with Change Data CaptureMapR Technologies
Machine learning (ML) and artificial intelligence (AI) enable intelligent processes that can autonomously make decisions in real-time. The real challenge for effective ML and AI is getting all relevant data to a converged data platform in real-time, where it can be processed using modern technologies and integrated into any downstream systems.
Machine Learning for Chickens, Autonomous Driving and a 3-year-old Who Won’t ...MapR Technologies
The document discusses machine learning and autonomous driving applications. It begins with a simple machine learning example of classifying images of chickens posted on Twitter. It then discusses how autonomous vehicles use machine learning by gathering large amounts of sensor data to train models for tasks like object recognition. The document also summarizes challenges for applying machine learning at an enterprise scale and how the MapR data platform can address these challenges by providing a unified environment for storing, accessing, and processing large amounts of diverse data.
ML Workshop 1: A New Architecture for Machine Learning LogisticsMapR Technologies
Having heard the high-level rationale for the rendezvous architecture in the introduction to this series, we will now dig in deeper to talk about how and why the pieces fit together. In terms of components, we will cover why streams work, why they need to be persistent, performant and pervasive in a microservices design and how they provide isolation between components. From there, we will talk about some of the details of the implementation of a rendezvous architecture including discussion of when the architecture is applicable, key components of message content and how failures and upgrades are handled. We will touch on the monitoring requirements for a rendezvous system but will save the analysis of the recorded data for later. Listen to the webinar on demand: https://mapr.com/resources/webinars/machine-learning-workshop-1/
Machine Learning Success: The Key to Easier Model ManagementMapR Technologies
Join Ellen Friedman, co-author (with Ted Dunning) of a new short O’Reilly book Machine Learning Logistics: Model Management in the Real World, to look at what you can do to have effective model management, including the role of stream-first architecture, containers, a microservices approach and a DataOps style of work. Ellen will provide a basic explanation of a new architecture that not only leverages stream transport but also makes use of canary models and decoy models for accurate model evaluation and for efficient and rapid deployment of new models in production.
Data Warehouse Modernization: Accelerating Time-To-Action MapR Technologies
Data warehouses have been the standard tool for analyzing data created by business operations. In recent years, increasing data volumes, new types of data formats, and emerging analytics technologies such as machine learning have given rise to modern data lakes. Connecting application databases, data warehouses, and data lakes using real-time data pipelines can significantly improve the time to action for business decisions. More: http://info.mapr.com/WB_MapR-StreamSets-Data-Warehouse-Modernization_Global_DG_17.08.16_RegistrationPage.html
Live Tutorial – Streaming Real-Time Events Using Apache APIsMapR Technologies
For this talk we will explore the power of streaming real time events in the context of the IoT and smart cities.
http://info.mapr.com/WB_Streaming-Real-Time-Events_Global_DG_17.08.02_RegistrationPage.html
Bringing Structure, Scalability, and Services to Cloud-Scale StorageMapR Technologies
Deploying storage with a forklift is so 1990s, right? Today’s applications and infrastructure demand systems and services that scale. Customers require performance and capacity to fit the use case and workloads, not the other way around. Architects need multi-temperature, multi-location, highly available, and compliance friendly platforms that grow with the generational shift in data growth and utility.
Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a service. Though originally used within the telecommunications industry, it has become common practice for banks, ISPs, insurance firms, and other verticals. More: http://info.mapr.com/WB_PredictingChurn_Global_DG_17.06.15_RegistrationPage.html
The prediction process is data-driven and often uses advanced machine learning techniques. In this webinar, we'll look at customer data, do some preliminary analysis, and generate churn prediction models – all with Spark machine learning (ML) and a Zeppelin notebook.
Spark’s ML library goal is to make machine learning scalable and easy. Zeppelin with Spark provides a web-based notebook that enables interactive machine learning and visualization.
In this tutorial, we'll do the following:
Review classification and decision trees
Use Spark DataFrames with Spark ML pipelines
Predict customer churn with Apache Spark ML decision trees
Use Zeppelin to run Spark commands and visualize the results
An Introduction to the MapR Converged Data PlatformMapR Technologies
Listen to the webinar on-demand: http://info.mapr.com/WB_Partner_CDP_Intro_EMEA_DG_17.05.31_RegistrationPage.html
In this 90-minute webinar, we discuss:
- The MapR Converged Data Platform and its components
- Use cases for the Converged Data Platform
- MapR Converged Partner Program
- How to get started with MapR
- Becoming a partner
How to Leverage the Cloud for Business Solutions | Strata Data Conference Lon...MapR Technologies
IT budgets are shrinking, and the move to next-generation technologies is upon us. The cloud is an option for nearly every company, but just because it is an option doesn’t mean it is always the right solution for every problem.
Most cloud providers would prefer that every customer be tightly coupled with their proprietary services and APIs to create lock-in with that cloud provider. The savvy customer will leverage the cloud as infrastructure and stay loosely bound to a cloud provider. This creates an opportunity for the customer to execute a multicloud strategy or even a hybrid on-premises and cloud solution.
Jim Scott explores different use cases that may be best run in the cloud versus on-premises, points out opportunities to optimize cost and operational benefits, and explains how to get the data moved between locations. Along the way, Jim discusses security, backups, event streaming, databases, replication, and snapshots across a variety of use cases that run most businesses today.
Is your organization at the analytics crossroads? Have you made strides collecting and sharing massive amounts of data from electronic health records, insurance claims, and health information exchanges but found these efforts made little impact on efficiency, patient outcomes, or costs?
Changes in how business is done combined with multiple technology drivers make geo-distributed data increasingly important for enterprises. These changes are causing serious disruption across a wide range of industries, including healthcare, manufacturing, automotive, telecommunications, and entertainment. Technical challenges arise with these disruptions, but the good news is there are now innovative solutions to address these problems. http://info.mapr.com/WB_Geo-distributed-Big-Data-and-Analytics_Global_DG_17.05.16_RegistrationPage.html
This document is the agenda for a MapR product update webinar that will take place in Spring 2017. It introduces MapR's new Persistent Application Client Container (PACC) which allows applications to easily persist data in Docker containers. It also discusses MapR Edge for IoT which extends MapR's converged data platform to the edge. The webinar will cover Hive, Spark, and Drill updates in the new MapR Ecosystem Pack 3.0. Speakers from MapR will provide details on these products and there will be a question and answer session.
3 Benefits of Multi-Temperature Data Management for Data AnalyticsMapR Technologies
SAP® HANA and SAP® IQ are popular platforms for various analytical and transactional use cases. If you’re an SAP customer, you’ve experienced the benefits of deploying these solutions. However, as data volumes grow, you’re likely asking yourself: How do I scale storage to support these applications? How can I have one platform for various applications and use cases?
Cisco & MapR bring 3 Superpowers to SAP HANA DeploymentsMapR Technologies
SAP HANA is an increasingly popular platform for various analytical and transactional use cases with its in-memory architecture. If you’re an SAP customer you’ve experienced the benefits.
However, the underlying storage for SAP HANA is painfully expensive. This slows down your ability to grow your SAP HANA footprint and serve up more applications.
You’re not the only one still loading your data into data warehouses and building marts or cubes out of it. But today’s data requires a much more accessible environment that delivers real-time results. Prepare for this transformation because your data platform and storage choices are about to undergo a re-platforming that happens once in 30 years.
With the MapR Converged Data Platform (CDP) and Cisco Unified Compute System (UCS), you can optimize today’s infrastructure and grow to take advantage of what’s next. Uncover the range of possibilities from re-platforming by intimately understanding your options for density, performance, functionality and more.
Drill can query JSON data stored in various data sources like HDFS, HBase, and Hive. It allows running SQL queries over JSON data without requiring a fixed schema. The document describes how Drill enables ad-hoc querying of JSON-formatted Yelp business review data using SQL, providing insights faster than traditional approaches.
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
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This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
"Building Future-Ready Apps with .NET 8 and Azure Serverless Ecosystem", Stan...Fwdays
.NET 8 brought a lot of improvements for developers and maturity to the Azure serverless container ecosystem. So, this talk will cover these changes and explain how you can apply them to your projects. Another reason for this talk is the re-invention of Serverless from a DevOps perspective as a Platform Engineering trend with Backstage and the recent Radius project from Microsoft. So now is the perfect time to look at developer productivity tooling and serverless apps from Microsoft's perspective.
This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
"Making .NET Application Even Faster", Sergey Teplyakov.pptxFwdays
In this talk we're going to explore performance improvement lifecycle, starting with setting the performance goals, using profilers to figure out the bottle necks, making a fix and validating that the fix works by benchmarking it. The talk will be useful for novice and seasoned .NET developers and architects interested in making their application fast and understanding how things work under the hood.
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.
UiPath Community Day Amsterdam: Code, Collaborate, ConnectUiPathCommunity
Welcome to our third live UiPath Community Day Amsterdam! Come join us for a half-day of networking and UiPath Platform deep-dives, for devs and non-devs alike, in the middle of summer ☀.
📕 Agenda:
12:30 Welcome Coffee/Light Lunch ☕
13:00 Event opening speech
Ebert Knol, Managing Partner, Tacstone Technology
Jonathan Smith, UiPath MVP, RPA Lead, Ciphix
Cristina Vidu, Senior Marketing Manager, UiPath Community EMEA
Dion Mes, Principal Sales Engineer, UiPath
13:15 ASML: RPA as Tactical Automation
Tactical robotic process automation for solving short-term challenges, while establishing standard and re-usable interfaces that fit IT's long-term goals and objectives.
Yannic Suurmeijer, System Architect, ASML
13:30 PostNL: an insight into RPA at PostNL
Showcasing the solutions our automations have provided, the challenges we’ve faced, and the best practices we’ve developed to support our logistics operations.
Leonard Renne, RPA Developer, PostNL
13:45 Break (30')
14:15 Breakout Sessions: Round 1
Modern Document Understanding in the cloud platform: AI-driven UiPath Document Understanding
Mike Bos, Senior Automation Developer, Tacstone Technology
Process Orchestration: scale up and have your Robots work in harmony
Jon Smith, UiPath MVP, RPA Lead, Ciphix
UiPath Integration Service: connect applications, leverage prebuilt connectors, and set up customer connectors
Johans Brink, CTO, MvR digital workforce
15:00 Breakout Sessions: Round 2
Automation, and GenAI: practical use cases for value generation
Thomas Janssen, UiPath MVP, Senior Automation Developer, Automation Heroes
Human in the Loop/Action Center
Dion Mes, Principal Sales Engineer @UiPath
Improving development with coded workflows
Idris Janszen, Technical Consultant, Ilionx
15:45 End remarks
16:00 Community fun games, sharing knowledge, drinks, and bites 🍻
Generative AI technology is a fascinating field that focuses on creating comp...Nohoax Kanont
Generative AI technology is a fascinating field that focuses on creating computer models capable of generating new, original content. It leverages the power of large language models, neural networks, and machine learning to produce content that can mimic human creativity. This technology has seen a surge in innovation and adoption since the introduction of ChatGPT in 2022, leading to significant productivity benefits across various industries. With its ability to generate text, images, video, and audio, generative AI is transforming how we interact with technology and the types of tasks that can be automated.