The document describes HBase's new timeline consistency feature for high availability reads using region replicas. It discusses how region replicas work, including that the primary region accepts writes while secondary replicas only allow reads. It introduces the new timeline consistency option for reads that returns results from secondary replicas, allowing for highly available reads. The implementation details and future work are also outlined.
Webinar: Deep Dive on Apache Flink State - Seth WiesmanVerverica
Apache Flink is a world class stateful stream processor presents a huge variety of optional features and configuration choices to the user. Determining out the optimal choice for any production environment and use-case be challenging. In this talk, we will explore and discuss the universe of Flink configuration with respect to state and state backends.
We will start with a closer look under the hood, at core data structures and algorithms, to build the foundation for understanding the impact of tuning parameters and the costs-benefit-tradeoffs that come with certain features and options. In particular, we will focus on state backend choices (Heap vs RocksDB), tuning checkpointing (incremental checkpoints, ...) and recovery (local recovery), serializers and Apache Flink's new state migration capabilities.
Apache kafka performance(throughput) - without data loss and guaranteeing dat...SANG WON PARK
Apache Kafak의 성능이 특정환경(데이터 유실일 발생하지 않고, 데이터 전송순서를 반드시 보장)에서 어느정도 제공하는지 확인��기 위한 테스트 결과 공유
데이터 전송순서를 보장하기 위해서는 Apache Kafka cluster로 partition을 분산할 수 없게되므로, 성능향상을 위한 장점을 사용하지 못하게 된다.
이번 테스트에서는 Apache Kafka의 단위 성능, 즉 partition 1개에 대한 성능만을 측정하게 된다.
향후, partition을 증가할 경우 본 테스트의 1개 partition 단위 성능을 기준으로 예측이 가능할 것 같다.
HBase and HDFS: Understanding FileSystem Usage in HBaseenissoz
This document discusses file system usage in HBase. It provides an overview of the three main file types in HBase: write-ahead logs (WALs), data files, and reference files. It describes durability semantics, IO fencing techniques for region server recovery, and how HBase leverages data locality through short circuit reads, checksums, and block placement hints. The document is intended help understand HBase's interactions with HDFS for tuning IO performance.
ZFS provides several advantages over traditional block-based filesystems when used with PostgreSQL, including preventing bitrot, improved compression ratios, and write locality. ZFS uses copy-on-write and transactional semantics to ensure data integrity and allow for snapshots and clones. Proper configuration such as enabling compression and using ZFS features like intent logging can optimize performance when used with PostgreSQL's workloads.
Tuning Apache Ambari performance for Big Data at scale with 3000 agentsDataWorks Summit
Apache Ambari manages Hadoop at large-scale and it becomes increasingly difficult for cluster admins to keep the machinery running smoothly as data grows and nodes scale from 30 to 3000 agents. To test at scale, Ambari has a Performance Stack that allows a VM to host as many as 50 Ambari Agents. The simulated stack and 50 Agents per VM can stress-test Ambari Server with the same load as a 3000 node cluster. This talk will cover how to tune the performance of Ambari and MySQL, and share performance benchmarks for features like deploy times, bulk operations, installation of bits, Rolling & Express Upgrade. Moreover, the speaker will show how to use Ambari Metrics System and Grafana to plot performance, detect anomalies, and pinpoint tips on how to improve performance for a more responsive experience. Lastly, the talk will discuss roadmap features in Ambari 3.0 for improving performance and scale.
This talk delves into the many ways that a user has to use HBase in a project. Lars will look at many practical examples based on real applications in production, for example, on Facebook and eBay and the right approach for those wanting to find their own implementation. He will also discuss advanced concepts, such as counters, coprocessors and schema design.
The document discusses Apache Tez, a framework for building data processing applications on Hadoop. It provides an introduction to Tez and describes key features like expressing computations as directed acyclic graphs (DAGs), container reuse, dynamic parallelism, integration with YARN timeline service, and recovery from failures. The document also outlines improvements to Tez around performance, debuggability, and status/roadmap.
The document discusses different types of block caches in HBase including LruBlockCache, SlabCache, and BucketCache. It explains that block caching improves performance by storing frequently accessed blocks in faster memory rather than slower disk storage. Each block cache has its own configuration options and memory usage characteristics. Benchmark results show that the off-heap BucketCache provides strong performance due to its use of off-heap memory for the L2 cache.
Producer Performance Tuning for Apache KafkaJiangjie Qin
Kafka is well known for high throughput ingestion. However, to get the best latency characteristics without compromising on throughput and durability, we need to tune Kafka. In this talk, we share our experiences to achieve the optimal combination of latency, throughput and durability for different scenarios.
Speaker: Jean-Daniel Cryans (Cloudera)
HBase Replication has come a long way since its inception in HBase 0.89 almost four years ago. Today, master-master and cyclic replication setups are supported; many bug fixes and new features like log compression, per-family peers configuration, and throttling have been added; and a major refactoring has been done. This presentation will recap the work done during the past four years, present a few use cases that are currently in production, and take a look at the roadmap.
Spark on YARN allows Spark jobs to run efficiently on YARN clusters. It supports two modes: yarn-client mode where the driver runs locally, and yarn-cluster mode where the driver runs in a YARN container. Dynamic resource allocation allows Spark to dynamically allocate containers based on workload, launching and killing executors as needed. This improves resource utilization by avoiding inefficient allocation where containers remain unused after tasks complete. Configuration changes are required to enable the external shuffle service to store RDD state externally rather than within executors.
Running Apache Kafka in production is only the first step in the Kafka operations journey. Professional Kafka users are ready to handle all possible disasters - because for most businesses having a disaster recovery plan is not optional.
In this session, we’ll discuss disaster scenarios that can take down entire Kafka clusters and share advice on how to plan, prepare and handle these events. This is a technical session full of best practices - we want to make sure you are ready to handle the worst mayhem that nature and auditors can cause.
Visit www.confluent.io for more information.
The document discusses Long-Lived Application Process (LLAP), a new capability in Apache Hive that enables long-lived daemon processes to improve query performance. LLAP eliminates Hive query startup costs by keeping query execution engines alive between queries. It allows queries to leverage just-in-time optimization and data caching to enable interactive query performance directly on HDFS data. LLAP utilizes asynchronous I/O, in-memory caching, and a query fragment API to optimize query processing. It integrates with Apache Tez to coordinate query execution across long-lived daemon processes and traditional YARN containers.
The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks.
To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library.
Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph.
A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML.
This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.
Capacity Planning Your Kafka Cluster | Jason Bell, DigitalisHostedbyConfluent
"There's little talk about capacity planning Kafka clusters, it's very much learn as you go, every cluster is different. In this talk Kafka DevOps Engineer Jason Bell takes you through the things that will help you, from broker capacity, thinking about topics and how the other Confluent components can affect throughput and performance. With a number of production deployments under his watchful gaze for over six years Jason has plenty of experience, stories and useful information that will help you.
By the end of the talk you'll have a good understanding of designing the cluster for various scenarios, where the points of latency are to watch and monitor. And also how to prevent teams breaking the cluster behind your back.
This talk is designed for everyone, anyone who is just starting to those who are operating Kafka on a daily basis."
Big Data means big hardware, and the less of it we can use to do the job properly, the better the bottom line. Apache Kafka makes up the core of our data pipelines at many organizations, including LinkedIn, and we are on a perpetual quest to squeeze as much as we can out of our systems, from Zookeeper, to the brokers, to the various client applications. This means we need to know how well the system is running, and only then can we start turning the knobs to optimize it. In this talk, we will explore how best to monitor Kafka and its clients to assure they are working well. Then we will dive into how to get the best performance from Kafka, including how to pick hardware and the effect of a variety of configurations in both the broker and clients. We’ll also talk about setting up Kafka for no data loss.
This document discusses strategies for scaling HBase to support millions of regions. It describes Yahoo's experience managing clusters with over 100,000 regions. Large regions can cause problems with tasks distribution, I/O contention during compaction, and scan timeouts. The document recommends keeping regions small and explores enhancements made in HBase to support very large region counts like splitting the meta region across servers and using hierarchical region directories to reduce load on the namenode. Performance tests show these changes improved the time to assign millions of regions.
HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Existing users of HBase/Phoenix as well as operators managing HBase clusters will benefit the most where they can learn about the new release and the long list of features. We will also briefly cover earlier 1.x release lines and compatibility and upgrade paths for existing users and conclude by giving an outlook on the next level of initiatives for the project.
This document provides an overview of optimizing and accelerating Ceph storage on Arm64 architecture. It discusses optimizations made to Ceph common libraries like UTF-8 handling and CRC calculation. It also covers integrating Ceph with technologies like SPDK and Seastar to improve performance. Benchmarks show performance gains from using 64K kernel pages and offloading storage functions to userspace frameworks. Overall, the document aims to improve the Ceph storage ecosystem on Arm servers through software and hardware optimizations.
Apache phoenix: Past, Present and Future of SQL over HBAseenissoz
HBase as the NoSQL database of choice in the Hadoop ecosystem has already been proven itself in scale and in many mission critical workloads in hundreds of companies. Phoenix as the SQL layer on top of HBase, has been increasingly becoming the tool of choice as the perfect complementary for HBase. Phoenix is now being used more and more for super low latency querying and fast analytics across a large number of users in production deployments. In this talk, we will cover what makes Phoenix attractive among current and prospective HBase users, like SQL support, JDBC, data modeling, secondary indexing, UDFs, and also go over recent improvements like Query Server, ODBC drivers, ACID transactions, Spark integration, etc. We will conclude by looking into items in the pipeline and how Phoenix and HBase interacts with other engines like Hive and Spark.
1) Columnar formats like Parquet, Kudu and Arrow provide more efficient data storage and querying by organizing data by column rather than row.
2) Parquet provides an immutable columnar format well-suited for storage, while Kudu allows for mutable updates but is optimized for scans. Arrow provides an in-memory columnar format focused on CPU efficiency.
3) By establishing common in-memory and on-disk columnar standards, Arrow and Parquet enable more efficient data sharing and querying across systems without serialization overhead.
Apache Phoenix and Apache HBase: An Enterprise Grade Data WarehouseJosh Elser
An overview of Apache Phoenix and Apache HBase from the angle of a traditional data warehousing solution. This talk focuses on where this open-source architect fits into the market outlines the features and integrations of the product, showing that it is a viable alternative to traditional data warehousing solutions.
Apache HBase is the Hadoop opensource, distributed, versioned storage manager well suited for random, realtime read/write access. This talk will give an overview on how HBase achieve random I/O, focusing on the storage layer internals. Starting from how the client interact with Region Servers and Master to go into WAL, MemStore, Compactions and on-disk format details. Looking at how the storage is used by features like snapshots, and how it can be improved to gain flexibility, performance and space efficiency.
Intro to HBase Internals & Schema Design (for HBase users)alexbaranau
This document provides an introduction to HBase internals and schema design for HBase users. It discusses the logical and physical views of HBase, including how tables are split into regions and stored across region servers. It covers best practices for schema design, such as using row keys efficiently and avoiding redundancy. The document also briefly discusses advanced topics like coprocessors and compression. The overall goal is to help HBase users optimize performance and scalability based on its internal architecture.
This document appears to be a list of students in Class 5 for the year 2014. It includes 31 students with their names, gender, parent/guardian names and marks for subjects UB2, pm and pn. The highest marks were 80 for both subjects by student Puteri Nur Suhaina bt Ahmad Suhaidi. The lowest marks were 35 for Sabbisha a/p Ganesh. There were 15 male students and 16 female students listed.
Who Watches the Watchmen - Arup Chakrabarti, PagerDuty - DevOpsDays Tel Aviv ...DevOpsDays Tel Aviv
The document summarizes a talk given at DevOps Days Chicago 2014 about monitoring tools and philosophies. It discusses using the right monitoring tools for different purposes like New Relic for application performance monitoring, StatsD and DataDog for customizable metrics and alerts, and SumoLogic for log monitoring. It also covers monitoring dependencies, distributed systems, security, and how the speaker's company PagerDuty validates their monitoring through "Failure Friday" tests.
O documento discute como os cristãos podem ter acesso à salvação em Cristo. Afirma que a Palavra de Deus, contida nas Escrituras, é essencial para conhecer e se relacionar com Cristo. Explica que a fé vem pelo ouvir da pregação da Palavra, e que invocar o nome do Senhor requer primeiro crer e ouvir a Palavra pregada. Conclui que a comunhão com Cristo só é possível por meio da Palavra de Deus nas Escrituras.
The document discusses a media production project involving a music video for the band Red Ocean Nectar. It summarizes:
1) The music video uses conventions of the rock genre, including live footage and close-ups of the band. It tells a narrative about domestic abuse that relates to the lyrics.
2) Feedback from an online survey informed major rewrites to the storyboard to include a stronger narrative and alternative interpretation of the lyrics.
3) Media technologies like YouTube, Premiere Pro, and Photoshop were used at different stages for research, production, and promotion. The final video was shared online and received generally positive feedback.
El documento describe el proceso de normalización de varias tablas relacionales. Explica cómo las tablas PRESTAMO, R, B, y EMPLEADO cumplen con la 1° y 2° forma normal, y propone su descomposición en 3° forma normal mediante la creación de nuevas tablas como Socio, Libro, R1, R2, ALUMNO, CURSO y DEPARTAMENTO.
O documento descreve a situação da Associação Melhores Amigos dos Animais (AMAIS) nos últimos 5 anos. A AMAIS cuidou de centenas de animais abandonados e vítimas de maus-tratos, mas atualmente enfrenta dificuldades financeiras e falta de voluntários. São pedidos apoio das autoridades públicas e cumprimento da lei para melhorar o atendimento aos animais.
JRuby on Rails Deployment: What They Didn't Tell Youelliando dias
This document summarizes a presentation on deploying JRuby on Rails applications. It discusses:
1) The mechanics of running Rails applications on JRuby and the Java virtual machine, including concurrency and threading considerations.
2) Preparations for deployment such as installing necessary gems, configuring databases, and examining dependencies.
3) Packaging applications into WAR files using the Warbler gem and configuring settings like the runtime pool size.
4) Additional post-deployment considerations for logging, sessions, caching, and performance.
The document provides information about recreational activities in Taitung, Taiwan, including bicycle tours, whale observation, and flower appreciation. It describes the Kwanshan Bicycle Path which is 12 km long and takes 1.5 hours to complete, noting the scenery along the path. It also describes the 8 km long Tsisan Bicycle Path and scenic views. Additionally, it mentions that the Huatung sea area is ideal for whale observation tours lasting around 3 hours and provides details about rape blossom viewing season from mid-January to February.
This document discusses how to make a moodboard in Photoshop by setting up a document, finding images from websites like patterntap.com, cutting and pasting the images into layers for backgrounds, colors and typography.
Transactions address the problems of concurrent database access and resilience to system failures. A transaction is defined as a sequence of SQL statements treated as a single unit. Transactions allow statements to appear isolated from each other to avoid inconsistencies during concurrent access, and ensure all-or-nothing execution of statements if a failure occurs. The combination of transactions with the ACID properties provides a solution for both concurrency control and recovery from failures.
The document discusses key ecommerce trends to watch for in 2015, including the continued growth of mobile buying and mobile marketing. It notes that mobile-friendly stores and apps will be important, as will ads that can be clicked on mobile devices. Other trends include using more personal content in marketing campaigns, the rise of social commerce reaching $14 billion in sales, and more online stores offering same-day shipping options.
HBase Read High Availability Using Timeline Consistent Region Replicasenissoz
This document summarizes a talk on implementing timeline consistency for HBase region replicas. It introduces the concept of region replicas, where each region has multiple copies hosted on different servers. The primary accepts writes, while secondary replicas are read-only. Reads from secondaries return possibly stale data. The talk outlines the implementation of region replicas in HBase, including updates to the master, region servers, and IPC. It discusses data replication approaches and next steps to implement write replication using the write-ahead log. The goal is to provide high availability for reads in HBase while tolerating single-server failures.
HBase Read High Availability Using Timeline-Consistent Region ReplicasHBaseCon
Speakers: Enis Soztutar and Devaraj Das (Hortonworks)
HBase has ACID semantics within a row that make it a perfect candidate for a lot of real-time serving workloads. However, single homing a region to a server implies some periods of unavailability for the regions after a server crash. Although the mean time to recovery has improved a lot recently, for some use cases, it is still preferable to do possibly stale reads while the region is recovering. In this talk, you will get an overview of our design and implementation of region replicas in HBase, which provide timeline-consistent reads even when the primary region is unavailable or busy.
The document summarizes Apache Phoenix and HBase as an enterprise data warehouse solution. It discusses how Phoenix provides OLTP and analytics capabilities over HBase. It then covers various use cases where companies are using Phoenix and HBase, including for web analytics and time series data. Finally, it discusses optimizations that can be made to the schema design, queries, and writes in Phoenix to improve performance.
Apache Hadoop 3.0 is coming! As the next major release, it attracts everyone's attention as show case several bleeding-edge technologies and significant features across all components of Apache Hadoop, include: Erasure Coding in HDFS, Multiple Standby NameNodes, YARN Timeline Service v2, JNI-based shuffle in MapReduce, Apache Slider integration and Service Support as First Class Citizen, Hadoop library updates and client-side class path isolation, etc.
In this talk, we will update the status of Hadoop 3 especially the releasing work in community and then go deep diving on new features included in Hadoop 3.0. As a new major release, Hadoop 3 would also include some incompatible changes - we will go through most of these changes and explore its impact to existing Hadoop users and operators. In the last part of this session, we will continue to discuss ongoing efforts in Hadoop 3 age and show the big picture that how big data landscape could be largely influenced by Hadoop 3.
The document summarizes Apache Phoenix and its past, present, and future as a SQL interface for HBase. It describes Phoenix's architecture and key features like secondary indexes, joins, aggregations, and transactions. Recent releases added functional indexes, the Phoenix Query Server, and initial transaction support. Future plans include improvements to local indexes, integration with Calcite and Hive, and adding JSON and other SQL features. The document aims to provide an overview of Phoenix's capabilities and roadmap for building a full-featured SQL layer over HBase.
This document summarizes a presentation about Apache Phoenix and HBase. It discusses the past, present, and future of SQL on HBase. In the past section, it describes Phoenix's architecture and key features like secondary indexes, joins, and aggregation. The present section highlights recent Phoenix releases including row timestamps, transactions using Tephra, and the new Phoenix Query Server. The future section mentions upcoming integrations with Calcite and Hive.
Apache HBase Internals you hoped you Never Needed to UnderstandJosh Elser
Covers numerous internal features, concepts, and implementations of Apache HBase. The focus will be driven from an operational standpoint, investigating each component enough to understand its role in Apache HBase and the generic problems that each are trying to solve. Topics will range from HBase’s RPC system to the new Procedure v2 framework, to filesystem and ZooKeeper use, to backup and replication features, to region assignment and row locks. Each topic will be covered at a high-level, attempting to distill the often complicated details down to the most salient information.
This talk with give and overview of exciting two releases for Apache HBase and Phoenix. HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the next evolution from the Apache HBase community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next biggest and most exciting milestone release because of Phoenix integration with Apache Calcite which ads lot of performance benefits with new query optimizer and helps to integrate with other data sources, especially those also based on calcite. It has lot of cool features such as Encoded columns, Kafka, Hive integration, improvements in secondary index rebuilding and many performance improvements.
Hadoop & cloud storage object store integration in production (final)Chris Nauroth
Today's typical Apache Hadoop deployments use HDFS for persistent, fault-tolerant storage of big data files. However, recent emerging architectural patterns increasingly rely on cloud object storage such as S3, Azure Blob Store, GCS, which are designed for cost-efficiency, scalability and geographic distribution. Hadoop supports pluggable file system implementations to enable integration with these systems for use cases such as off-site backup or even complex multi-step ETL, but applications may encounter unique challenges related to eventual consistency, performance and differences in semantics compared to HDFS. This session explores those challenges and presents recent work to address them in a comprehensive effort spanning multiple Hadoop ecosystem components, including the Object Store FileSystem connector, Hive, Tez and ORC. Our goal is to improve correctness, performance, security and operations for users that choose to integrate Hadoop with Cloud Storage. We use S3 and S3A connector as case study.
This document discusses Hadoop integration with cloud storage. It describes the Hadoop-compatible file system architecture, which allows Hadoop applications to work with both HDFS and cloud storage transparently. Recent enhancements to the S3A file system connector for Amazon S3 are discussed, including performance improvements and support for encryption. Benchmark results show significant performance gains for Hive queries with S3A compared to earlier versions. Upcoming work on output committers, object store abstraction, and consistency are outlined.
Apache Hadoop 3 is coming! As the next major milestone for hadoop and big data, it attracts everyone's attention as showcase several bleeding-edge technologies and significant features across all components of Apache Hadoop: Erasure Coding in HDFS, Docker container support, Apache Slider integration and Native service support, Application Timeline Service version 2, Hadoop library updates and client-side class path isolation, etc. In this talk, first we will update the status of Hadoop 3.0 releasing work in apache community and the feasible path through alpha, beta towards GA. Then we will go deep diving on each new feature, include: development progress and maturity status in Hadoop 3. Last but not the least, as a new major release, Hadoop 3.0 will contain some incompatible API or CLI changes which could be challengeable for downstream projects and existing Hadoop users for upgrade - we will go through these major changes and explore its impact to other projects and users.
Speaker: Sanjay Radia, Founder and Chief Architect, Hortonworks
Disaster Recovery and Cloud Migration for your Apache Hive WarehouseSankar H
This document discusses disaster recovery and cloud migration strategies for Apache Hive data warehouses. It covers replication modes like master-slave and master-master. It also discusses fail over, fail back, event-based replication using REPL commands, change management, bootstrapping, and demonstrations. Challenges of cloud migration like data movement and integrity are also addressed. The future of Hive replication including ACID tables, faster bootstrapping, and table-level replication is outlined.
The document discusses Hadoop integration with cloud storage. It describes the Hadoop-compatible file system architecture, which allows applications to work with different storage systems transparently. Recent enhancements to the S3A connector for Amazon S3 are discussed, including performance improvements and support for encryption. Benchmark results show significant performance gains for Hive queries running on S3A compared to earlier versions. Upcoming work on consistency, output committers, and abstraction layers is outlined to further improve object store integration.
Dancing elephants - efficiently working with object stores from Apache Spark ...DataWorks Summit
As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different,
What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them.
We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you.
This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.
Apache Hadoop YARN is a modern resource-management platform that can host multiple data processing engines for various workloads like batch processing (MapReduce), interactive SQL (Hive, Tez), real-time processing (Storm), existing services and a wide variety of custom applications. These applications can all co-exist on YARN and share a single data center in a cost-effective manner with the platform worrying about resource management, isolation and multi-tenancy.
YARN is now adding support for services in a first class manner. This talk will first cover the challenges of running services on YARN, and then move on to the changes that were made to the ResourceManager to support scheduling services on YARN(such as affinity and anti-affinity). The talk will then move on to cover the changes made in the NodeManager and features such as container restart and container upgrades. The talk will also cover new additions to YARN like the new application manager (that will allow users to bring services workloads onto YARN by providing features such as container orchestration and management) and the DNS server that uses the YARN registry to enable service discovery.
Cloudy with a chance of Hadoop - real world considerationsDataWorks Summit
Over the last eighteen months, we have seen significant adoption of Hadoop eco-system centric big data processing in Microsoft Azure and Amazon AWS. In this talk we present some of the lessons learned and architectural considerations for cloud-based deployments including security, fault tolerance and auto-scaling.
We look at how Hortonworks Data Cloud and Cloudbreak can automate that scaling of Hadoop clusters, showing how it can react dynamically to workloads, and what that can deliver in cost-effective Hadoop-in-cloud deployments.
FOD Paris Meetup - Global Data Management with DataPlane Services (DPS)Abdelkrim Hadjidj
The architecture of modern enterprise data lakes is based on multiple Hadoop clusters. Several clusters are used to separate between environments such as dev, test, production and DR. In some organizations, each business line has its own dedicated cluster to comply with legal or internal constraints. This is often the case in financial services. Hybrid cloud deployment is another example of multi-clusters deployment that we see in several verticals such as manufacturing. In this presentation, we will introduce Dataplane Service (DPS), a global data management platform that enables organizations to operate, secure and govern multiple clusters from a single pane of glass. We will show how DPS is a foundation of any services that needs to operate on multiple clusters. Finally, we will present three services on top of DPS and we will focus on Data Replication and Disaster Recovery.
Keynote slides from Big Data Spain Nov 2016. Has some thoughts on how Hadoop ecosystem is growing and changing to support the enterprise, including Hive, Spark, NiFi, security and governance, streaming, and the cloud.
Deep learning on yarn running distributed tensorflow etc on hadoop cluster v3DataWorks Summit
Deep learning is useful for enterprises tasks in the field of speech recognition, image classification, AI chatbots, and machine translation, just to name a few.
In order to train deep learning/machine learning models, applications such as TensorFlow, MXNet, Caffe, and XGBoost can be leveraged. And sometimes these applications will be used together to solve different problems.
To make distributed deep learning/machine learning applications easily launched, managed, and monitored, we introduced, in Apache Hadoop 3.x, YARN native services along with other improvements such as first-class GPU support, container-DNS support, scheduling improvements, etc. These improvements make distributed deep learning/machine learning applications run on YARN as simple as running it locally, which can let machine learning engineers focus on algorithms instead of worrying about underlying infrastructure. Also, YARN can better manage a shared cluster which runs deep learning/machine learning and other services and ETL jobs with these improvements.
In this session, we will take a closer look at these improvements and show how to run these applications on YARN with demos. Audiences can start trying running these applications on YARN after this talk.
Speakers
Wanga Tan, Staff Software Engineer, Hortonworks
Sunil Govindan, Staff Engineer, Hortonworks
Similar to HBase Read High Availabilty using Timeline Consistent Region Replicas (20)
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Danny Chen presented on Uber's use of HBase for global indexing to support large-scale data ingestion. Uber uses HBase to provide a global view of datasets ingested from Kafka and other data sources. To generate indexes, Spark jobs are used to transform data into HFiles, which are loaded into HBase tables. Given the large volumes of data, techniques like throttling HBase access and explicit serialization are used. The global indexing solution supports requirements for high throughput, strong consistency and horizontal scalability across Uber's data lake.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
This document discusses using Apache NiFi to build a high-speed cyber security data pipeline. It outlines the challenges of ingesting, transforming, and routing large volumes of security data from various sources to stakeholders like security operations centers, data scientists, and executives. It proposes using NiFi as a centralized data gateway to ingest data from multiple sources using a single entry point, transform the data according to destination needs, and reliably deliver the data while avoiding issues like network traffic and data duplication. The document provides an example NiFi flow and discusses metrics from processing over 20 billion events through 100+ production flows and 1000+ transformations.
Supporting Apache HBase : Troubleshooting and Supportability ImprovementsDataWorks Summit
This document discusses supporting Apache HBase and improving troubleshooting and supportability. It introduces two Cloudera employees who work on HBase support and provides an overview of typical troubleshooting scenarios for HBase like performance degradation, process crashes, and inconsistencies. The agenda covers using existing tools like logs and metrics to troubleshoot HBase performance issues with a general approach, and introduces htop as a real-time monitoring tool for HBase.
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
Demystifying Neural Networks And Building Cybersecurity ApplicationsPriyanka Aash
In today's rapidly evolving technological landscape, Artificial Neural Networks (ANNs) have emerged as a cornerstone of artificial intelligence, revolutionizing various fields including cybersecurity. Inspired by the intricacies of the human brain, ANNs have a rich history and a complex structure that enables them to learn and make decisions. This blog aims to unravel the mysteries of neural networks, explore their mathematical foundations, and demonstrate their practical applications, particularly in building robust malware detection systems using Convolutional Neural Networks (CNNs).
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
Choosing the Best Outlook OST to PST Converter: Key Features and Considerationswebbyacad software
When looking for a good software utility to convert Outlook OST files to PST format, it is important to find one that is easy to use and has useful features. WebbyAcad OST to PST Converter Tool is a great choice because it is simple to use for anyone, whether you are tech-savvy or not. It can smoothly change your files to PST while keeping all your data safe and secure. Plus, it can handle large amounts of data and convert multiple files at once, which can save you a lot of time. It even comes with 24*7 technical support assistance and a free trial, so you can try it out before making a decision. Whether you need to recover, move, or back up your data, Webbyacad OST to PST Converter is a reliable option that gives you all the support you need to manage your Outlook data effectively.
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
Increase Quality with User Access Policies - July 2024Peter Caitens
⭐️ Increase Quality with User Access Policies ⭐️, presented by Peter Caitens and Adam Best of Salesforce. View the slides from this session to hear all about “User Access Policies” and how they can help you onboard users faster with greater quality.
Self-Healing Test Automation Framework - HealeniumKnoldus Inc.
Revolutionize your test automation with Healenium's self-healing framework. Automate test maintenance, reduce flakes, and increase efficiency. Learn how to build a robust test automation foundation. Discover the power of self-healing tests. Transform your testing experience.
Retrieval Augmented Generation Evaluation with RagasZilliz
Retrieval Augmented Generation (RAG) enhances chatbots by incorporating custom data in the prompt. Using large language models (LLMs) as judge has gained prominence in modern RAG systems. This talk will demo Ragas, an open-source automation tool for RAG evaluations. Christy will talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.
Keynote : AI & Future Of Offensive SecurityPriyanka Aash
In the presentation, the focus is on the transformative impact of artificial intelligence (AI) in cybersecurity, particularly in the context of malware generation and adversarial attacks. AI promises to revolutionize the field by enabling scalable solutions to historically challenging problems such as continuous threat simulation, autonomous attack path generation, and the creation of sophisticated attack payloads. The discussions underscore how AI-powered tools like AI-based penetration testing can outpace traditional methods, enhancing security posture by efficiently identifying and mitigating vulnerabilities across complex attack surfaces. The use of AI in red teaming further amplifies these capabilities, allowing organizations to validate security controls effectively against diverse adversarial scenarios. These advancements not only streamline testing processes but also bolster defense strategies, ensuring readiness against evolving cyber threats.
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
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
"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.