DIscover Spark and Spark streaming
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This document provides an overview of Spark, including how it differs from MapReduce by loading more data into memory and implementing caching mechanisms. It discusses Spark's ecosystem, features like RDDs, and how it can run on Hadoop clusters. It also summarizes Spark Streaming, describing how it processes data in micro-batches, provides windowing and transformation operations on DStreams, and unifies batch, streaming, and interactive analytics.
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This talk is about sharing experience and lessons learned on setting up and running the Apache Spark service inside the database group at CERN. It covers the many aspects of this change with examples taken from use cases and projects at the CERN Hadoop, Spark, streaming and database services. The talks is aimed at developers, DBAs, service managers and members of the Spark community who are using and/or investigating “Big Data” solutions deployed alongside relational database processing systems. The talk highlights key aspects of Apache Spark that have fuelled its rapid adoption for CERN use cases and for the data processing community at large, including the fact that it provides easy to use APIs that unify, under one large umbrella, many different types of data processing workloads from ETL, to SQL reporting to ML.
Spark can also easily integrate a large variety of data sources, from file-based formats to relational databases and more. Notably, Spark can easily scale up data pipelines and workloads from laptops to large clusters of commodity hardware or on the cloud. The talk also addresses some key points about the adoption process and learning curve around Apache Spark and the related “Big Data” tools for a community of developers and DBAs at CERN with a background in relational database operations.
Webinar: Dyn + DataStax - helping companies deliver exceptional end-user expe...
Dyn delivers exceptional Internet Performance. Enabling high quality services requires data centers around the globe. In order to manage services, customers need timely insight collected from all over the world. Dyn uses DataStax Enterprise (DSE) to deploy complex clusters across multiple datacenters to enable sub 50 ms query responses for hundreds of billions of data points. From granular DNS traffic data, to aggregated counts for a variety of report dimensions, DSE at Dyn has been up since 2013 and has shined through upgrades, data center migrations, DDoS attacks and hardware failures. In this webinar, Principal Engineers Tim Chadwick and Rick Bross cover the requirements which led them to choose DSE as their go-to Big Data solution, the path which led to SPARK, and the lessons that we’ve learned in the process.
Spark Summit EU talk by Mike Percy
The document discusses Apache Kudu, an open source storage layer for Apache Hadoop that enables fast analytics on fast data. Kudu is designed to fill the gap between HDFS and HBase by providing fast analytics capabilities on fast-changing or frequently updated data. It achieves this through its scalable and fast tabular storage design that allows for both high insert/update throughput and fast scans/queries. The document provides an overview of Kudu's architecture and capabilities, examples of how to use its NoSQL and SQL APIs, and real-world use cases like enabling low-latency analytics pipelines for companies like Xiaomi.
Scylla Summit 2022: Building Zeotap's Privacy Compliant Customer Data Platfor...
Customer Data Platforms, commonly called CDPs, form an integral part of the marketing stack powering Zeotap's Adtech and Martech use-cases. The company offers a privacy-compliant CDP platform, and ScyllaDB is an integral part. Zeotap's CDP demands a mix of OLTP, OLAP, and real-time data ingestion, requiring a highly-performant store.
In this presentation, Shubham Patil, Lead Software Engineer, and Safal Pandita, Senior Software Engineer at Zeotap will share how ScyllaDB is powering their solution and why it's a great fit. They begin by describing their business use case and the challenges they were facing before moving to ScyllaDB. Then they cover their technical use-cases and requirements for real-time and batch data ingestions. They delve into our data access patterns and describe their data model supporting all use cases simultaneously for ingress/egress. They explain how they are using Scylla Migrator for our migration needs, then describe their multiregional, multi-tenant production setup for onboarding more than 130+ partners. Finally, they finish by sharing some of their learnings, performance benchmarks, and future plans.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
MongoDB vs Scylla: Production Experience from Both Dev & Ops Standpoint at Nu...
This document compares MongoDB and ScyllaDB databases. It discusses their histories, architectures, data models, querying capabilities, consistency handling, and scaling approaches. It also provides takeaways for operations teams and developers, noting that ScyllaDB favors consistent performance over flexibility while MongoDB is more flexible but sacrifices some performance. The document also outlines how a company called Numberly uses both MongoDB and ScyllaDB for different use cases.
A New “Sparkitecture” for Modernizing your Data Warehouse: Spark Summit East ...
Legacy enterprise data warehouse (EDW) architecture, geared toward day-to-day workloads associated with operational querying, reporting, and analytics, are often ill-equipped to handle the volume of data, traffic, and varied data types associated with a modern, ad-hoc analytics platform. Faced with challenges of increasing pipeline speed, aggregation, and visualization in a simplified, self-service fashion, organizations are increasingly turning to some combination of Spark, Hadoop, Kafka, and proven analytical databases like Vertica as key enabling technologies to optimize their EDW architecture. Join us to learn how successful organizations have developed real-time streaming solutions with these technologies for range of use cases, including IOT predictive maintenance.
Zeotap: Moving to ScyllaDB - A Graph of Billions Scale
Zeotap’s Connect product addresses the challenges of identity resolution and linking for AdTech and MarTech. Zeotap manages roughly 20 billion ID and growing. In their presentation, Zeotap engineers will delve into data access patterns, processing and storage requirements to make a case for a graph-based store. They will share the results of PoCs made on technologies such as D-graph, OrientDB, Aeropike and Scylla, present the reasoning for selecting JanusGraph backed by Scylla, and take a deep dive into their data model architecture from the point of ingestion. Learn what is required for the production setup, configuration and performance tuning to manage data at this scale.
Cassandra vs. ScyllaDB: Evolutionary Differences
Apache Cassandra and ScyllaDB are distributed databases capable of processing massive globally-distributed workloads. Both use the same CQL data query language. In this webinar you will learn:
- How are they architecturally similar and how are they different?
- What's the difference between them in performance and features?
- How do their software lifecycles and release cadences contrast?
Scylla Summit 2022: Operating at Monstrous Scales: Benchmarking Petabyte Work...
ScyllaDB is a distributed database designed to scale horizontally and vertically — in theory. What about in practice? ScyllaDB’s Benny Halevy, Director, Software Engineering, will take you through the process and results of benchmarking our NoSQL database at the petabyte level, showing how you can use advanced features like workload prioritization to control priorities of transactional (read-write) and analytic (read-only) queries on the same cluster with smooth and predictable performance.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
Scylla Summit 2018: Keynote - 4 Years of Scylla
This document summarizes Dor Laor's experience over 4+ years with ScyllaDB, including key milestones and achievements as well as ongoing goals and challenges. It notes Scylla's initial release in 2016 and improvements over time to features such as materialized views and global secondary indexes. It also discusses optimizing performance on cloud infrastructure and addressing challenges related to workload types and capacity planning. Going forward, it outlines priorities like lightweight transactions, change data capture, and improving Cassandra compatibility. The overall message is one of pride in accomplishments while still feeling challenged to achieve further dreams and improvements.
Overcoming Barriers of Scaling Your Database
Scaling distributed databases successfully requires meeting myriad challenges from physical distribution of your data across on-premises locations, public cloud vendors, geographies and political entities to adopting technologies to overcome fundamental operational bottlenecks. Join ScyllaDB's Peter Corless, director of technical advocacy, as he interviews Moreno Garcia y Silva, head of solution architecture, about how to navigate both technical ecosystem and database architectural challenges for this next tech cycle.
Takeaways:
- Recognizing and classifying barriers to scaling
- Solutions to overcome scaling challenges
- Upfront planning and real-time response
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Big Data Day LA 2015 - Sparking up your Cassandra Cluster- Analytics made Awe...
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Stream, Stream, Stream: Different Streaming Methods with Spark and Kafka
At NMC (Nielsen Marketing Cloud) we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences.
To achieve that, we need to ingest billions of events per day into our big data stores, and we need to do it in a scalable yet cost-efficient manner.
In this session, we will discuss how we continuously transform our data infrastructure to support these goals.
Specifically, we will review how we went from CSV files and standalone Java applications all the way to multiple Kafka and Spark clusters, performing a mixture of Streaming and Batch ETLs, and supporting 10x data growth.
We will share our experience as early-adopters of Spark Streaming and Spark Structured Streaming, and how we overcame technical barriers (and there were plenty...).
We will present a rather unique solution of using Kafka to imitate streaming over our Data Lake, while significantly reducing our cloud services' costs.
Topics include :
* Kafka and Spark Streaming for stateless and stateful use-cases
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Spark Driven Big Data Analytics
This document provides an overview of Spark driven big data analytics. It begins by defining big data and its characteristics. It then discusses the challenges of traditional analytics on big data and how Apache Spark addresses these challenges. Spark improves on MapReduce by allowing distributed datasets to be kept in memory across clusters. This enables faster iterative and interactive processing. The document outlines Spark's architecture including its core components like RDDs, transformations, actions and DAG execution model. It provides examples of writing Spark applications in Java and Java 8 to perform common analytics tasks like word count.
Stream, stream, stream: Different streaming methods with Spark and Kafka
Going into different streaming methods, we will share our experience as early-adopters of Spark Streaming and Spark Structured Streaming, and how we overcame technical barriers (and there were plenty...).
We will also present a rather unique solution of using Kafka to imitate streaming over our Data Lake, while significantly reducing our cloud services’ costs.
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The Future of Hadoop: A deeper look at Apache Spark
Jai Ranganathan, Senior Director of Product Management, discusses why Spark has experienced such wide adoption and provide a technical deep dive into the architecture. Additionally, he presents some use cases in production today. Finally, he shares our vision for the Hadoop ecosystem and why we believe Spark is the successor to MapReduce for Hadoop data processing.
Processing Large Data with Apache Spark -- HasGeek
Apache Spark presentation at HasGeek FifthElelephant
https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark
Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries
Scalable Monitoring Using Prometheus with Apache Spark Clusters with Diane F...
As Apache Spark applications move to a containerized environment, there are many questions about how to best configure server systems in the container world. In this talk we will demonstrate a set of tools to better monitor performance and identify optimal configuration settings. We will demonstrate how Prometheus, a project that is now part of the Cloud Native Computing Foundation (CNCF: https://www.cncf.io/projects/), can be applied to monitor and archive system performance data in a containerized spark environment.
In our examples, we will gather spark metric output through Prometheus and present the data with Grafana dashboards. We will use our examples to demonstrate how performance can be enhanced through different tuned configuration settings. Our demo will show how to configure settings across the cluster as well as within each node.
From Pipelines to Refineries: scaling big data applications with Tim Hunter
Big data tools are challenging to combine into a larger application: ironically, big data applications themselves do not tend to scale very well. These issues of integration and data management are only magnified by increasingly large volumes of data. Apache Spark provides strong building blocks for batch processes, streams and ad-hoc interactive analysis. However, users face challenges when putting together a single coherent pipeline that could involve hundreds of transformation steps, especially when confronted by the need of rapid iterations. This talk explores these issues through the lens of functional programming. It presents an experimental framework that provides full-pipeline guarantees by introducing more laziness to Apache Spark. This framework allows transformations to be seamlessly composed and alleviates common issues, thanks to whole program checks, auto-caching, and aggressive computation parallelization and reuse.
Stream Data Processing at Big Data Landscape by Oleksandr Fedirko
This document provides an overview of stream data processing and common stream processing tools. It discusses streaming basics like stateful and stateless operations. It also covers microbatch vs realtime streaming and compositional vs declarative stream processing engines. Typical stream processing architectures and use cases are presented. Main considerations for projects using stream processing are outlined. An overview of popular stream processing tools like Apache Spark, Storm, Flink, and cloud services from AWS, GCP and Azure is provided. The document concludes with a case study example and questions for discussion.
Healthcare Claim Reimbursement using Apache Spark
The document discusses rewriting a claims reimbursement system using Spark. It describes how Spark provides better performance, scalability and cost savings compared to the previous Oracle-based system. Key points include using Spark for ETL to load data into a Delta Lake data lake, implementing the business logic in a reusable Java library, and seeing significant increases in processing volumes and speeds compared to the prior system. Challenges and tips for adoption are also provided.
Unified Big Data Processing with Apache Spark
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1yNuLGF.
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code. Filmed at qconsf.com.
Matei Zaharia is an assistant professor of computer science at MIT, and CTO of Databricks, the company commercializing Apache Spark.
AWS Big Data Demystified #1: Big data architecture lessons learned
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company
The video: https://youtu.be/l5KmaZNQxaU
dont forget to subcribe to the youtube channel
The website: https://amazon-aws-big-data-demystified.ninja/
The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/
The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
Headaches and Breakthroughs in Building Continuous Applications
At SpotX, we have built and maintained a portfolio of Spark Streaming applications -- all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We'll detail what we've learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.
Spark + AI Summit 2019: Headaches and Breakthroughs in Building Continuous Ap...
At SpotX, we have built and maintained a portfolio of Spark Streaming applications -- all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We'll detail what we've learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.
Presented by Landon Robinson and Jack Chapa
Glint with Apache Spark
Spark Overview, Cluster Architecture, Elements, Spark Stack, Spark Streaming
Meetup Details of my presentation:
http://www.meetup.com/lspe-in/events/212250542/
http://www.meetup.com/devops-bangalore/events/222155834/
Spark Summit EU talk by Ahsan Javed Awan
The document discusses performance characterization of in-memory data analytics using Apache Spark on a scale-up server. It identifies problems like poor multicore scalability, thread load imbalance, I/O wait times, and GC overhead. Solutions proposed include NUMA awareness, hyperthreading, disabling next-line prefetchers, using parallel scavenge GC, multiple small executors, and a future node architecture based on a hybrid in-storage processing and 2D processing-in-memory design. The work aims to improve node-level performance through architecture support for emerging big data workloads.
Apache Spark Fundamentals
This document provides an overview of the Apache Spark framework. It covers Spark fundamentals including the Spark execution model using Resilient Distributed Datasets (RDDs), basic Spark programming, and common Spark libraries and use cases. Key topics include how Spark improves on MapReduce by operating in-memory and supporting general graphs through its directed acyclic graph execution model. The document also reviews Spark installation and provides examples of basic Spark programs in Scala.
SnappyData Toronto Meetup Nov 2017
SnappyData is a new open source project started by Pivotal GemFire founders to provide a unified platform for OLTP, OLAP and streaming analytics using Spark. It aims to simplify big data architectures by supporting mixed workloads in a single clustered database, allowing for real-time operational analytics on live data without copying to other systems. This provides faster insights than current approaches that require periodic data copying between different databases and analytics systems.
Ops Jumpstart: MongoDB Administration 101
New to MongoDB? We'll provide an overview of installation, high availability through replication, scale out through sharding, and options for monitoring and backup. No prior knowledge of MongoDB is assumed. This session will jumpstart your knowledge of MongoDB operations, providing you with context for the rest of the day's content.
Cassandra at Pollfish
Pollfish is a survey platform which provides access to millions of targeted users. Pollfish allows easy distribution and targeting of surveys through existing mobile apps. (https://www.pollfish.com/). At pollfish we use Cassandra for difference use cases, eg. for application data store to maximize write throughput when appropriate and for our analytics project to find insights in application generated data. As a medium to accomplish our success so far, we use the Datastax's DSE 4.6 environment which integrates Appache Cassadra, Spark and a hadoop compatible file system (CFS). We will discuss how we started, how the journey was and the impressions gained so far along with some tips learned the hard way. This is a result of joint work of an excellent team here at Pollfish.
Cassandra at Pollfish
Cassandra at Pollfish. Athens Cassandra Users MeetUp (Datastax)- 11 March 2015.
http://www.meetup.com/Athens-Cassandra-Users/events/220922960/
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Stream, Stream, Stream: Different Streaming Methods with Spark and Kafka
Stream, Stream, Stream: Different Streaming Methods with Spark and Kafka
Stream, stream, stream: Different streaming methods with Spark and Kafka
Stream, stream, stream: Different streaming methods with Spark and Kafka
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The Future of Hadoop: A deeper look at Apache Spark
Processing Large Data with Apache Spark -- HasGeek
Processing Large Data with Apache Spark -- HasGeek
Scalable Monitoring Using Prometheus with Apache Spark Clusters with Diane F...
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From Pipelines to Refineries: scaling big data applications with Tim Hunter
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Stream Data Processing at Big Data Landscape by Oleksandr Fedirko
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AWS Big Data Demystified #1: Big data architecture lessons learned
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Headaches and Breakthroughs in Building Continuous Applications
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Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
一比一原版(CU毕业证)卡尔顿大学毕业证如何办理
原件一模一样【微信：95270640】【卡尔顿大学毕业证CU学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
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6:个人职称评审加20分
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
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Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
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A population analysis specific to Bataan.
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版制作【微信:41543339】【多伦多大学毕业证(UofT毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
UofT毕业证如何办理
原件一模一样【微信：95270640】【多伦多大学毕业证UofT学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理多伦多大学毕业证UofT学位证硕士毕业证成绩单【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
一比一原版(UO毕业证)渥太华大学毕业证如何办理
原件一模一样【微信：95270640】【渥太华大学毕业证UO学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证UO学位证毕业证offer【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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DIscover Spark and Spark streaming
- 1. TechLabs by A la découverte de Machine Learning, de Redis et de Spark
- 4. Outline ❏ Data processing today ❏ Spark, hadoop, MapReduce ❏ Spark ecosystem ❏ Spark basics
- 5. Data processing today Data intensive application Definition : “We call an application data-intensive if data is its primary challenge—the quantity of data, the complexity of data, or the speed at which it is changing—as opposed to compute-intensive, where CPU cycles are the bottleneck.” Martin Klepmann
- 6. Data processing today Today apps needs : ❏ Store data (databases) ❏ Caches ❏ search data (search index) ❏ Asynchronously message handling (stream processing) ❏ batch processing
- 8. Spark, hadoop, MapReduce Spark : main differences with Map Reduce ❏ Spark load most of the dataset in memory ❏ Implement cache mechanisms which reduce read from disk ❏ Is much faster than MapReduce : Job scheduling ❏ Does not implement any data distribution technology but can run on top of hadoop clusters (HDFS )
- 9. Spark ecosystem : open source
- 10. Spark ecosystem : features
- 11. Spark ecosystem : deployment
- 12. Spark basics : RDD RDD : Resilient Distributed data ❏ Primary spark abstraction ❏ Fault tolerant collection of elements ❏ Partitioned and Immutable ❏ Two types operations ❏ Lazy Transformation
- 13. Spark basics : An execution flow
- 14. Spark Streaming
- 15. Outline ❏ Why In-stream processing ? ❏ Runtime and Programming Model ❏ Spark Streaming : Overview ❏ Benefits of Discretized Stream Processing ❏ Processing flow ❏ Transform operations ❏ Window operations
- 16. Why In-stream processing ?
- 17. Why In-stream processing ?
- 18. Runtime and Programming Model Native Streaming
- 19. Runtime and Programming Model Micro-batch Streaming
- 20. Spark Streaming : Overview
- 21. Benefits of Discretized Stream Processing Dynamic load balancing
- 22. Benefits of Discretized Stream Processing Fast failure and straggler recovery
- 23. Benefits of Discretized Stream Processing ❏ Unification of batch, streaming and interactive analytics ❏ Advanced analytics like machine learning and interactive SQL ❏ Streaming + SQL and DataFrames ❏ Streaming + MLlib
- 24. Spark Streaming : Processing flow
- 25. Spark Streaming : DStreams Discretized Streams (DStreams) : ❏ The basic spark streaming abstraction ❏ A continuous series of RDDs
- 26. Spark Streaming : Transformations Transform Operations : Any operation applied on a DStream translates to operations on the underlying RDDs
- 27. Spark Streaming : Transformations Window Operations :
- 28. Spark Streaming : Time abstractions Batch interval Sliding interval Window size
- 29. Spark Streaming : Time abstractions Batch interval Window size Sliding interval
- 30. Spark Streaming : Some examples ❏ Wordcount ❏ stateless operation, counting words for every batch ❏ Basic Error count ❏ stateless operation, using a filter : contains(“ERROR”) ❏ Cumulative Error count ❏ Stateful operation, errors from the beginning of the processing ❏ Windowed Errors counts ❏ Stateful operation, errors from the sliding window of time