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.
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.
Performance Optimizations in Apache ImpalaCloudera, Inc.
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard components (HDFS, HBase, Metastore, Sentry) and is able to read the majority of the widely-used file formats (e.g. Parquet, Avro, RCFile).
To reduce latency, such as that incurred from utilizing MapReduce or by reading data remotely, Impala implements a distributed architecture based on daemon processes that are responsible for all aspects of query execution and that run on the same machines as the rest of the Hadoop infrastructure. Impala employs runtime code generation using LLVM in order to improve execution times and uses static and dynamic partition pruning to significantly reduce the amount of data accessed. The result is performance that is on par or exceeds that of commercial MPP analytic DBMSs, depending on the particular workload. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. AWS S3), Apache Kudu and HBase. In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
ORC files were originally introduced in Hive, but have now migrated to an independent Apache project. This has sped up the development of ORC and simplified integrating ORC into other projects, such as Hadoop, Spark, Presto, and Nifi. There are also many new tools that are built on top of ORC, such as Hive’s ACID transactions and LLAP, which provides incredibly fast reads for your hot data. LLAP also provides strong security guarantees that allow each user to only see the rows and columns that they have permission for.
This talk will discuss the details of the ORC and Parquet formats and what the relevant tradeoffs are. In particular, it will discuss how to format your data and the options to use to maximize your read performance. In particular, we’ll discuss when and how to use ORC’s schema evolution, bloom filters, and predicate push down. It will also show you how to use the tools to translate ORC files into human-readable formats, such as JSON, and display the rich metadata from the file including the type in the file and min, max, and count for each column.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
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.
Performance Optimizations in Apache ImpalaCloudera, Inc.
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard components (HDFS, HBase, Metastore, Sentry) and is able to read the majority of the widely-used file formats (e.g. Parquet, Avro, RCFile).
To reduce latency, such as that incurred from utilizing MapReduce or by reading data remotely, Impala implements a distributed architecture based on daemon processes that are responsible for all aspects of query execution and that run on the same machines as the rest of the Hadoop infrastructure. Impala employs runtime code generation using LLVM in order to improve execution times and uses static and dynamic partition pruning to significantly reduce the amount of data accessed. The result is performance that is on par or exceeds that of commercial MPP analytic DBMSs, depending on the particular workload. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. AWS S3), Apache Kudu and HBase. In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
ORC files were originally introduced in Hive, but have now migrated to an independent Apache project. This has sped up the development of ORC and simplified integrating ORC into other projects, such as Hadoop, Spark, Presto, and Nifi. There are also many new tools that are built on top of ORC, such as Hive’s ACID transactions and LLAP, which provides incredibly fast reads for your hot data. LLAP also provides strong security guarantees that allow each user to only see the rows and columns that they have permission for.
This talk will discuss the details of the ORC and Parquet formats and what the relevant tradeoffs are. In particular, it will discuss how to format your data and the options to use to maximize your read performance. In particular, we’ll discuss when and how to use ORC’s schema evolution, bloom filters, and predicate push down. It will also show you how to use the tools to translate ORC files into human-readable formats, such as JSON, and display the rich metadata from the file including the type in the file and min, max, and count for each column.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
This presentation shortly describes key features of Apache Cassandra. It was held at the Apache Cassandra Meetup in Vienna in January 2014. You can access the meetup here: http://www.meetup.com/Vienna-Cassandra-Users/
At Salesforce, we have deployed many thousands of HBase/HDFS servers, and learned a lot about tuning during this process. This talk will walk you through the many relevant HBase, HDFS, Apache ZooKeeper, Java/GC, and Operating System configuration options and provides guidelines about which options to use in what situation, and how they relate to each other.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
HBaseCon 2015: Taming GC Pauses for Large Java Heap in HBaseHBaseCon
In this presentation, we will introduce Hotspot's Garbage First collector (G1GC) as the most suitable collector for latency-sensitive applications running with large memory environments. We will first discuss G1GC internal operations and tuning opportunities, and also cover tuning flags that set desired GC pause targets, change adaptive GC thresholds, and adjust GC activities at runtime. We will provide several HBase case studies using Java heaps as large as 100GB that show how to best tune applications to remove unpredicted, protracted GC pauses.
Power of the Log: LSM & Append Only Data Structuresconfluent
This talk is about the beauty of sequential access and append-only data structures. We'll do this in the context of a little-known paper entitled “Log Structured Merge Trees”. LSM describes a surprisingly counterintuitive approach to storing and accessing data in a sequential fashion. It came to prominence in Google's Big Table paper and today, the use of Logs, LSM and append-only data structures drive many of the world's most influential storage systems: Cassandra, HBase, RocksDB, Kafka and more. Finally, we'll look at how the beauty of sequential access goes beyond database internals, right through to how applications communicate, share data and scale.
Apache Arrow is designed to make things faster. Its focused on speeding communication between systems as well as processing within any one system. In this talk I'll start by discussing what Arrow is and why it was built. This will include covering an overview of the key components, goals, vision and current state. I’ll then take the audience through a detailed engineering review of how we used Arrow to solve several problems when building the Apache-Licensed Dremio product. This will include talking about Arrow performance characteristics, working with Arrow APIs, managing memory, sizing Arrow vectors, and moving data between processes and/or nodes. We’ll also review several code examples of specific data processing implementations and how they interact with Arrow data. Lastly we’ll spend a short amount of time on what’s next for Arrow. This will be a highly technical talk targeted towards people building data infrastructure systems and complex workflows.
Near-realtime analytics with Kafka and HBasedave_revell
A presentation at OSCON 2012 by Nate Putnam and Dave Revell about Urban Airship's analytics stack. Features Kafka, HBase, and Urban Airship's own open source projects statshtable and datacube.
This presentation shortly describes key features of Apache Cassandra. It was held at the Apache Cassandra Meetup in Vienna in January 2014. You can access the meetup here: http://www.meetup.com/Vienna-Cassandra-Users/
At Salesforce, we have deployed many thousands of HBase/HDFS servers, and learned a lot about tuning during this process. This talk will walk you through the many relevant HBase, HDFS, Apache ZooKeeper, Java/GC, and Operating System configuration options and provides guidelines about which options to use in what situation, and how they relate to each other.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
HBaseCon 2015: Taming GC Pauses for Large Java Heap in HBaseHBaseCon
In this presentation, we will introduce Hotspot's Garbage First collector (G1GC) as the most suitable collector for latency-sensitive applications running with large memory environments. We will first discuss G1GC internal operations and tuning opportunities, and also cover tuning flags that set desired GC pause targets, change adaptive GC thresholds, and adjust GC activities at runtime. We will provide several HBase case studies using Java heaps as large as 100GB that show how to best tune applications to remove unpredicted, protracted GC pauses.
Power of the Log: LSM & Append Only Data Structuresconfluent
This talk is about the beauty of sequential access and append-only data structures. We'll do this in the context of a little-known paper entitled “Log Structured Merge Trees”. LSM describes a surprisingly counterintuitive approach to storing and accessing data in a sequential fashion. It came to prominence in Google's Big Table paper and today, the use of Logs, LSM and append-only data structures drive many of the world's most influential storage systems: Cassandra, HBase, RocksDB, Kafka and more. Finally, we'll look at how the beauty of sequential access goes beyond database internals, right through to how applications communicate, share data and scale.
Apache Arrow is designed to make things faster. Its focused on speeding communication between systems as well as processing within any one system. In this talk I'll start by discussing what Arrow is and why it was built. This will include covering an overview of the key components, goals, vision and current state. I’ll then take the audience through a detailed engineering review of how we used Arrow to solve several problems when building the Apache-Licensed Dremio product. This will include talking about Arrow performance characteristics, working with Arrow APIs, managing memory, sizing Arrow vectors, and moving data between processes and/or nodes. We’ll also review several code examples of specific data processing implementations and how they interact with Arrow data. Lastly we’ll spend a short amount of time on what’s next for Arrow. This will be a highly technical talk targeted towards people building data infrastructure systems and complex workflows.
Near-realtime analytics with Kafka and HBasedave_revell
A presentation at OSCON 2012 by Nate Putnam and Dave Revell about Urban Airship's analytics stack. Features Kafka, HBase, and Urban Airship's own open source projects statshtable and datacube.
Hanborq Optimizations on Hadoop MapReduceHanborq Inc.
A Hanborq optimized Hadoop Distribution, especially with high performance of MapReduce. It's the core part of HDH (Hanborq Distribution with Hadoop for Big Data Engineering).
1 Introduction at CloudStack Developer Day
1 - Introduction at CloudStack Developer Day
By Alex Huang
Architect, Cloud Platforms Group, Citrix Systems Inc.
In this session you will learn:
HBase Introduction
Row & Column storage
Characteristics of a huge DB
What is HBase?
HBase Data-Model
HBase vs RDBMS
HBase architecture
HBase in operation
Loading Data into HBase
HBase shell commands
HBase operations through Java
HBase operations through MR
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
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 hbase for the enterprise (Strata+Hadoop World 2012)jmhsieh
10/25/12. My talk on the features and updates added in the past year to Apache HBase that are important for enterprises. This includes overviews of mechanisms for faster recovery, better recovery detection, replication and data backup strategies.
Siebel Server Cloning available in 8.1.1.9 / 8.2.2.2Jeroen Burgers
Installation Cloning
Siebel server cloning
Enterprise cloning
Patch Deployment
Capture installation changes
Apply changes to target environments
Server Configuration Deployment
Extract server configuration settings
Migrate server configurations to target environments
Strata + Hadoop World 2012: Apache HBase Features for the EnterpriseCloudera, Inc.
Apache HBase is a distributed data store that is in production today at many enterprises and sites serving large volumes of near-real-time random-accesses. As Apache HBase matures, the community has augmented the system with new features that many enterprise consider to be hard requirements. We will discuss how the upcoming HBase 0.96 release addresses many of these shortcomings by introducing new features that will help the administrator monitor and control access to the system, and new mechanisms to minimize downtime due to expected and unexpected outages.
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
Data serves as the platform for decision-making at Uber. To facilitate data driven decisions, many datasets at Uber are ingested in a Hadoop Data Lake and exposed to querying via Hive. Analytical queries joining various datasets are run to better understand business data at Uber.
Data ingestion, at its most basic form, is about organizing data to balance efficient reading and writing of newer data. Data organization for efficient reading involves factoring in query patterns to partition data to ensure read amplification is low. Data organization for efficient writing involves factoring the nature of input data - whether it is append only or updatable.
At Uber we ingest terabytes of many critical tables such as trips that are updatable. These tables are fundamental part of Uber's data-driven solutions, and act as the source-of-truth for all the analytical use-cases across the entire company. Datasets such as trips constantly receive updates to the data apart from inserts. To ingest such datasets we need a critical component that is responsible for bookkeeping information of the data layout, and annotates each incoming change with the location in HDFS where this data should be written. This component is called as Global Indexing. Without this component, all records get treated as inserts and get re-written to HDFS instead of being updated. This leads to duplication of data, breaking data correctness and user queries. This component is key to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. This component will need to have strong consistency and provide large throughputs for index writes and reads.
At Uber, we have chosen HBase to be the backing store for the Global Indexing component and is a critical component in allowing us to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. In this talk, we will discuss data@Uber and expound more on why we built the global index using Apache Hbase and how this helps to scale out our cluster usage. We’ll give details on why we chose HBase over other storage systems, how and why we came up with a creative solution to automatically load Hfiles directly to the backend circumventing the normal write path when bootstrapping our ingestion tables to avoid QPS constraints, as well as other learnings we had bringing this system up in production at the scale of data that Uber encounters daily.
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
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
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.
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Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
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https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
HBase Storage Internals
1. HBase
Storage
Internals,
present
and
future!
Ma6eo
Bertozzi
|
@Cloudera
March
2013
-‐
Hadoop
Summit
Europe
1
2. What
is
HBase
• Open
source
Storage
Manager
that
provides
random
read/write
on
top
of
HDFS
• Provides
Tables
with
a
“Key:Column/Value”
interface
• Dynamic
columns
(qualifiers),
no
schema
needed
• “Fixed”
column
groups
(families)
• table[row:family:column]
=
value
2
3. HBase
EcoSystem
• Apache
Hadoop
HDFS
for
data
durability
and
reliability
(Write-‐Ahead
App
MR
Log)
• Apache
ZooKeeper
for
distributed
coordina]on
ZK
HDFS
• Apache
Hadoop
MapReduce
built-‐in
support
for
running
MapReduce
jobs
3
5. Master,
Region
Servers
and
Regions
• Region
Server
Client
• Server
that
contains
a
set
of
Regions
ZooKeeper
• Responsible
to
handle
reads
and
writes
• Region
Master
• The
basic
unit
of
scalability
in
HBase
• Subset
of
the
table’s
data
Region
Server
Region
Server
Region
Server
• Con]guous,
sorted
range
of
rows
stored
Region
Region
Region
together.
Region
Region
Region
• Master
Region
Region
Region
• Coordinates
the
HBase
Cluster
HDFS
• Assignment/Balancing
of
the
Regions
• Handles
admin
opera]ons
• create/delete/modify
table,
…
5
6. Autosharding
and
.META.
table
• A
Region
is
a
Subset
of
the
table’s
data
• When
there
is
too
much
data
in
a
Region…
• a
split
is
triggered,
crea]ng
2
regions
• The
associa]on
“Region
-‐>
Server”
is
stored
in
a
System
Table
• The
Loca]on
of
.META.
Is
stored
in
ZooKeeper
Table
Start
Key
Region
ID
Region
Server
machine01
Region
1
-‐
testTable
testTable
Key-‐00
1
machine01.host
Region
4
-‐
testTable
testTable
Key-‐31
2
machine03.host
machine02
testTable
Key-‐65
3
machine02.host
Region
3
-‐
testTable
testTable
Key-‐83
4
machine01.host
Region
1
-‐
users
…
…
…
…
machine03
users
Key-‐AB
1
machine03.host
Region
2
-‐
testTable
users
Key-‐KG
2
machine02.host
Region
2
-‐
users
6
7. The
Write
Path
–
Create
a
New
Table
• The
client
asks
to
the
master
to
create
a
new
Table
• hbase>
create
‘myTable’,
‘cf’
Client
createTable()
• The
Master
Master
• Store
the
Table
informa]on
(“schema”)
Store
Table
“Metadata”
• Create
Regions
based
on
the
key-‐splits
provided
Assign
the
Regions
“enable”
• no
splits
provided,
one
single
region
by
Region
Region
Region
default
Server
Region
Server
Server
Region
Region
• Assign
the
Regions
to
the
Region
Servers
Region
Region
Region
• The
assignment
Region
-‐>
Server
is
wri6en
to
a
system
table
called
“.META.”
7
8. The
Write
Path
–
“Inser]ng”
data
Client
• table.put(row-‐key:family:column,
value)
Where
is
.META.?
Scan
.META.
• The
client
asks
ZooKeeper
the
loca]on
of
.META.
ZooKeeper
Region
Server
Insert
Region
• The
client
scans
.META.
searching
for
the
KeyValue
Region
Region
Server
responsible
to
handle
the
Key
Region
Server
Region
• The
client
asks
the
Region
Server
to
Region
insert/update/delete
the
specified
key/value.
Region
• The
Region
Server
process
the
request
and
dispatch
it
to
the
Region
responsible
to
handle
the
Key
• The
opera]on
is
wri6en
to
a
Write-‐Ahead
Log
(WAL)
• …and
the
KeyValues
added
to
the
Store:
“MemStore”
8
9. The
Write
Path
–
Append
Only
to
Random
R/W
• Files
in
HDFS
are
RS
Region
WAL
Region
Region
• Append-‐Only
• Immutable
once
closed
MemStore
+
Store
Files
(HFiles)
• HBase
provides
Random
Writes?
• …not
really
from
a
storage
point
of
view
• KeyValues
are
stored
in
memory
and
wri6en
to
disk
on
pressure
• Don’t
worry
your
data
is
safe
in
the
WAL!
Key0
–
value
0
• (The
Region
Server
can
recover
data
from
the
WAL
is
case
of
crash)
Key1
–
value
1
Key2
–
value
2
Key3
–
value
3
But
this
allow
to
sort
data
by
Key
before
wri]ng
on
disk
•
Key4
–
value
4
Key5
–
value
5
• Deletes
are
like
Inserts
but
with
a
“remove
me
flag”
Store
Files
9
10. The
Read
Path
–
“reading”
data
• The
client
asks
ZooKeeper
the
loca]on
of
.META.
Client
Where
is
• The
client
scans
.META.
searching
for
the
Region
Server
.META.?
Scan
.META.
responsible
to
handle
the
Key
ZooKeeper
Region
Server
Region
• The
client
asks
the
Region
Server
to
get
the
specified
key/
Get
Key
Region
value.
Region
Server
• The
Region
Server
process
the
request
and
dispatch
it
to
Region
Region
the
Region
responsible
to
handle
the
Key
Region
• MemStore
and
Store
Files
are
scanned
to
find
the
key
10
11. The
Read
Path
–
Append
Only
to
Random
R/W
• Each
flush
a
new
file
is
created
Key0
–
value
0.0
Key0
–
value
0.1
Key2
–
value
2.0
Key5
–
value
5.0
Key3
–
value
3.0
Key1
–
value
1.0
Key5
–
value
5.0
Key5
–
[deleted]
Key8
–
value
8.0
Key6
–
value
6.0
• Each
file
have
KeyValues
sorted
by
key
Key9
–
value
9.0
Key7–
value
7.0
• Two
or
more
files
can
contains
the
same
key
(updates/deletes)
• To
find
a
Key
you
need
to
scan
all
the
files
• …with
some
op]miza]ons
• Filter
Files
Start/End
Key
• Having
a
bloom
filter
on
each
file
11
13. HFile
format
Blocks
• Only
Sequen]al
Writes,
just
append(key,
value)
Header
• Large
Sequen]al
Reads
are
be6er
Record
0
Record
1
• Why
grouping
records
in
blocks?
Key/Value
…
(record)
Record
N
• Easy
to
split
Key
Length
:
int
Header
Value
Length
:
int
Record
0
• Easy
to
read
Key
:
byte[]
Record
1
…
• Easy
to
cache
Value
:
byte[]
Record
N
Index
0
• Easy
to
index
(if
records
are
sorted)
…
Index
N
• Block
Compression
(snappy,
lz4,
gz,
…)
Trailer
13
14. Data
Block
Encoding
• “Be
aware
of
the
data”
• Block
Encoding
allows
to
compress
the
Key
based
on
what
we
know
• Keys
are
sorted…
prefix
may
be
similar
in
most
cases
• One
file
contains
keys
from
one
Family
only
• Timestamps
are
“similar”,
we
can
store
the
diff
“on-‐disk”
• Type
is
“put”
most
of
the
]me…
KeyValue
Row
Length
:
short
Row
:
byte[]
Family
Length
:
byte
Family
:
byte[]
Qualifier
:
byte[]
Timestamp
:
long
Type
:
byte
14
16. Compac]ons
• Reduce
the
number
of
files
to
look
into
during
a
scan
Key0
–
value
0.0
Key0
–
value
0.1
Key2
–
value
2.0
Key1
–
value
1.0
Key3
–
value
3.0
Key4–
value
4.0
Key5
–
value
5.0
Key5
–
[deleted]
Key8
–
value
8.0
Key6
–
value
6.0
• Removing
duplicated
keys
(updated
values)
Key9
–
value
9.0
Key7–
value
7.0
• Removing
deleted
keys
Key0
–
value
0.1
Key1
–
value
1.0
• Creates
a
new
file
by
merging
the
content
of
2+
files
Key2
–
value
2.0
Key4–
value
4.0
Key6
–
value
6.0
Key7–
value
7.0
Key8–
value
8.0
• Remove
the
old
files
Key9–
value
9.0
16
17. Pluggable
Compac]ons
• Try
different
algorithm
Key0
–
value
0.0
Key0
–
value
0.1
Key2
–
value
2.0
Key1
–
value
1.0
Key3
–
value
3.0
Key4–
value
4.0
Key5
–
value
5.0
Key5
–
[deleted]
Key8
–
value
8.0
Key6
–
value
6.0
• Be
aware
of
the
data
Key9
–
value
9.0
Key7–
value
7.0
• Time
Series?
I
guess
no
updates
from
the
80s
Key0
–
value
0.1
• Be
aware
of
the
requests
Key1
–
value
1.0
Key2
–
value
2.0
Key4–
value
4.0
Key6
–
value
6.0
Key7–
value
7.0
• Compact
based
on
sta]s]cs
Key8–
value
8.0
Key9–
value
9.0
• which
files
are
hot
and
which
are
not
• which
keys
are
hot
and
which
are
not
17
18. Snapshots
Zero-‐copy
snapshots
and
table
clones
18
19. How
taking
a
snapshot
works?
• The
master
orchestrate
the
RSs
• the
communica]on
is
done
via
ZooKeeper
• using
a
“2-‐phase
commit
like”
transac]on
(prepare/commit)
• Each
RS
is
responsible
to
take
its
“piece”
of
snapshot
• For
each
Region
store
the
metadata
informa]on
needed
• (list
of
Store
Files,
WALs,
region
start/end
keys,
…)
ZK
ZK
Master
ZK
RS
RS
Region
WAL
Region
Region
Region
WAL
Region
Region
Store
Files
(HFiles)
Store
Files
(HFiles)
19
20. What
is
a
Snapshots?
• “a
Snapshot
is
not
a
copy
of
the
table”
• a
Snapshot
is
a
set
of
metadata
informa]on
• The
table
“schema”
(column
families
and
a6ributes)
• The
Regions
informa]on
(start
key,
end
key,
…)
• The
list
of
Store
Files
• The
list
of
WALs
ac]ve
ZK
ZK
Master
ZK
RS
RS
Region
WAL
Region
Region
Region
WAL
Region
Region
Store
Files
(HFiles)
Store
Files
(HFiles)
20
21. Cloning
a
Table
from
a
Snapshots
• hbase>
clone_snapshot
‘snapshotName’,
‘tableName’
…
• Creates
a
new
table
with
the
data
“contained”
in
the
snapshot
• No
data
copies
involved
• HFiles
are
immutable
• And
shared
between
tables
and
snapshots
• You
can
insert/update/remove
data
from
the
new
table
• No
repercussions
on
the
snapshot,
original
tables
or
other
cloned
tables
21
22. Compac]ons
&
Archiving
• HFiles
are
immutable,
and
shared
between
tables
and
snapshots
• On
compac]on
or
table
dele]on,
files
are
removed
from
disk
• If
files
are
referenced
by
a
snapshot
or
a
cloned
table
• The
file
is
moved
to
an
“archive”
directory
• And
deleted
later,
when
there’re
no
references
to
it
22
24. 0.96
is
coming
up
• Moving
RPC
to
Protobuf
• Allows
rolling
upgrades
with
no
surprises
• HBase
Snapshots
• Pluggable
Compac]ons
• Remove
-‐ROOT-‐
• Table
Locks
24
25. 0.98
and
Beyond
• Transparent
Table/Column-‐Family
Encryp]on
• Cell-‐level
security
• Mul]ple
WALs
per
Region
Server
(MTTR)
• Data
Placement
Awareness
(MTTR)
• Data
Type
Awareness
• Compac]on
policies,
based
on
the
data
needs
• Managing
blocks
directly
(instead
of
files)
25