Meet HBase 2.0 and Phoenix 5.0
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This talk will give an overview of two exciting releases for Apache HBase 2.0 and Phoenix 5.0. HBase provides a NoSQL column store on Hadoop for random, real-time read/write workloads. Phoenix provides SQL on top of HBase. HBase 2.0 contains a large number of features that were a long time in development, including rewritten region assignment, performance improvements (RPC, rewritten write pipeline, etc), async clients and WAL, a C++ client, offheaping memstore and other buffers, shading of dependencies, as well as a lot of other fixes and stability improvements. We will go into details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next big Phoenix release because of its integration with HBase 2.0 and a lot of performance improvements in support of secondary Indexes. It has many important new features such as encoded columns, Kafka and Hive integration, and many other performance improvements. This session will also describe the uses cases that HBase and Phoenix are a good architectural fit for. Speaker: Alan Gates, Co-Founder, Hortonworks
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In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
Hortonworks Data in Motion Webinar Series Part 7 Apache Kafka Nifi Better Tog...
Apache NiFi, Storm and Kafka augment each other in modern enterprise architectures. NiFi provides a coding free solution to get many different formats and protocols in and out of Kafka and compliments Kafka with full audit trails and interactive command and control. Storm compliments NiFi with the capability to handle complex event processing.
Join us to learn how Apache NiFi, Storm and Kafka can augment each other for creating a new dataplane connecting multiple systems within your enterprise with ease, speed and increased productivity.
https://www.brighttalk.com/webcast/9573/224063
Data in the Cloud Crash Course
Introduction
This workshop is a hands-on session to quickly deploy Hadoop and Streaming on AWS / Azure / Google Cloud.
Cloudbreak simplifies the deployment of Hadoop in cloud environments. It enables the enterprise to quickly run big data workloads in the cloud while optimizing the use of cloud resources.
Format
A short introductory lecture about Cloudbreak. This is followed by a walk through and lab leveraging Hadoop and Streaming in the Cloud with Cloudbreak.
Objective
To provide a quick and short hands-on introduction to Hadoop on the cloud. Review key benefits of cluster deployment automation.
This lab will use Cloudbreak to quickly and effortlessly stand up Hadoop and Streaming clusters in a cloud provider of your choice. The lab shows the use of Ambari blueprints that are your declarative definitions of your Hadoop or Streaming clusters. Steps to dynamically change these blueprints and use external databases and external authentication sources and in essence showing a way to provide Shared Authentication, Authorization and Audit across ephemeral and long-lasting clusters. However it is not limited to only custom blueprints, the lab also shows how Cloudbreak provides easy to use custom scripts called recipes that can be executed before or after Ambari start or after cluster installation.
Hive present-and-feature-shanghai
1) Apache Hive Present
2) How Hive Achieved 100x Performance
3) Hive sub-second Response
Apache Nifi Crash Course
Introduction: This workshop will provide a hands on introduction to simple event data processing and data flow processing using a Sandbox on students’ personal machines.
Format: A short introductory lecture to Apache NiFi and computing used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache NiFi. In the lab, you will install and use Apache NiFi to collect, conduct and curate data-in-motion and data-at-rest with NiFi. You will learn how to connect and consume streaming sensor data, filter and transform the data and persist to multiple data sources.
Best practices and lessons learnt from Running Apache NiFi at Renault
No real-time insight without real-time data ingestion. No real-time data ingestion without NiFi ! Apache NiFi is an integrated platform for data flow management at entreprise level, enabling companies to securely acquire, process and analyze disparate sources of information (sensors, logs, files, etc) in real-time. NiFi helps data engineers accelerate the development of data flows thanks to its UI and a large number of powerful off-the-shelf processors. However, with great power comes great responsibilities. Behind the simplicity of NiFi, best practices must absolutely be respected in order to scale data flows in production & prevent sneaky situations. In this joint presentation, Hortonworks and Renault, a French car manufacturer, will present lessons learnt from real world projects using Apache NiFi. We will present NiFi design patterns to achieve high level performance and reliability at scale as well as the process to put in place around the technology for data flow governance. We will also show how these best practices can be implemented in practical use cases and scenarios.
Speakers
Kamelia Benchekroun, Data Lake Squad Lead, Renault Group
Abdelkrim Hadjidj, Solution Engineer, Hortonworks
Running Enterprise Workloads in the Cloud
Demand for cloud is through the roof. Cloud is turbo charging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
In this session, we will take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through how the latest from Cloudbreak enables enterprises to easily and securely run big data workloads. This includes deep-dive discussion on autoscaling, Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As a last topic we will discuss how we deployed and operate Cloudbreak as a Service internally which enables rapid cluster deployment for prototyping and testing purposes.
Speakers
Peter Darvasi, Cloudbreak Partner Engineer, Hortonworks
Richard Doktorics, Staff Engineer, Hortonworks
Meet HBase 2.0 and Phoenix-5.0
This talk will give an overview of two exciting releases for Apache HBase and Phoenix. HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2018. It is the next evolution from the Apache HBase community after 1.0. HBase-2.0 contains a large number of features that are long time in development, some of which include rewritten region assignment , perf improvements (RPC, rewritten write pipeline, etc), async clients and WAL, C++ client, offheaping memstore and other buffers, shading of dependencies, as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next biggest release because of its integration with HBase 2.0 and lot of performance improvements in support of secondary Indexes. It has a lot of cool features such as encoded columns, Kafka, Hive integration, and many other performance improvements. Ankit Singhal, Senior Software Engineer, Hortonworks Inc. and Rajeshbabu Chintaguntl, Staff Software Engineer, Hortonworks
Building Data Pipelines for Solr with Apache NiFi
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It supports highly configurable directed graphs of data routing, transformation, and system mediation logic. Some of NiFi's key features include a web-based user interface for monitoring and controlling data flows, guaranteed delivery, data provenance, and easy extensibility through custom processor development.
These features make NiFi a perfect candidate for building production quality data pipelines that interact with Apache Solr. This talk will demonstrate how to use a NiFi processor that delivers data to a Solr update handler, as well as a processor for extracting data from Solr on regular intervals for delivery to down-stream systems. In addition we will show how these processors can be combined with other built-in NiFi processors to solve a variety of use cases, including log aggregation, and indexing messages received from Kafka.
Connecting the Drops with Apache NiFi & Apache MiNiFi
Demand for increased capture of information to drive analytic insights into an organizations' assets and infrastructure is growing at unprecedented rates. However, as data volume growth soars, the ability to provide seamless ingestion pipelines becomes operationally complex as the magnitude of data sources and types expands.
This talk will focus on the efforts of the Apache NiFi community including subproject, MiNiFi; an agent based architecture and its relation to the core Apache NiFi project. MiNiFi is focused on providing a platform that meets and adapts to where data is born while providing the core tenets of NiFi in provenance, security, and command and control. These capabilities provide versatile avenues for the bi-directional exchange of information across data and control planes while dealing with the constraints of operation at opposite ends of the scale spectrum tackling the first and last miles of dataflow management.
We will highlight ongoing and new efforts in the community to provide greater flexibility with deployment and configuration management of flows. Versioned flows provide greater operational flexibility and serve as a powerful foundation to orchestrate the collection and transmission from the point of data's inception through to its transmission to consumers and processing systems.
Major advancements in Apache Hive towards full support of SQL compliance
Major advancements in Apache Hive towards full support of SQL complianceDataWorks Summit/Hadoop Summit
Major advancements in Apache Hive towards full support of SQL compliance
Running Apache NiFi with Apache Spark : Integration Options
A walk-through of various options in integration Apache Spark and Apache NiFi in one smooth dataflow. There are now several options in interfacing between Apache NiFi and Apache Spark with Apache Kafka and Apache Livy.
What's hot (20)
Dataflow Management From Edge to Core with Apache NiFi
Dataflow Management From Edge to Core with Apache NiFi
Using Spark Streaming and NiFi for the Next Generation of ETL in the Enterprise
Using Spark Streaming and NiFi for the Next Generation of ETL in the Enterprise
Hortonworks Data in Motion Webinar Series Part 7 Apache Kafka Nifi Better Tog...
Hortonworks Data in Motion Webinar Series Part 7 Apache Kafka Nifi Better Tog...
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Best practices and lessons learnt from Running Apache NiFi at Renault
Connecting the Drops with Apache NiFi & Apache MiNiFi
Connecting the Drops with Apache NiFi & Apache MiNiFi
Major advancements in Apache Hive towards full support of SQL compliance
Major advancements in Apache Hive towards full support of SQL compliance
Running Apache NiFi with Apache Spark : Integration Options
Running Apache NiFi with Apache Spark : Integration Options
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SoCal BigData Day
SQL on Hadoop Batch, Interactive and Beyond.
Public Presentation showing history and where Hortonworks is looking to go with 100% Open Source Technology.
Apache Hive, Apache SparkSQL, Apache Pheonix, and Apache Druid
De-Mystifying the Apache Phoenix QueryServer
A general introduction to the Apache Phoenix QueryServer. It aims to cover the "what", "why", and "how" behind the technology.
Achieving Mega-Scale Business Intelligence Through Speed of Thought Analytics...
SpringOne Platform 2016
Speaker: Ian Fyfe; Director, Product Marketing, Hortonworks
Apache Hadoop is the most powerful and popular platform for ingesting, storing and processing enormous amounts of “big data”. However, due to its original roots as a batch processing system, doing interactive business analytics with Hadoop has historically suffered from slow response times, or forced business analysts to extract data summaries out of Hadoop into separate data marts. This talk will discuss the different options for implementing speed-of-thought business analytics and machine learning tools directly on top of Hadoop including Apache Hive on Tez, Apache Hive on LLAP, Apache HAWQ and Apache MADlib.
Meet HBase 2.0 and Phoenix 5.0
This talk with give and overview of exciting two releases for Apache HBase and Phoenix. HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the next evolution from the Apache HBase community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next biggest and most exciting milestone release because of Phoenix integration with Apache Calcite which ads lot of performance benefits with new query optimizer and helps to integrate with other data sources, especially those also based on calcite. It has lot of cool features such as Encoded columns, Kafka, Hive integration, improvements in secondary index rebuilding and many performance improvements.
Hortonworks Technical Workshop: HBase and Apache Phoenix
HBASE is the leading NoSQL database. Tightly integrated with Hadoop ecosystem, it offers random, real-time read/write capabilities on billions of rows and millions of columns. Apache Phoenix offers a SQL interface to HBASE, opening HBase to large community of SQL developers and enabling inter-operability with SQL compliant applications. The session will cover the essentials of HBASE and provide an in-depth insight into Apache Phoenix. Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community. Recording:
https://hortonworks.webex.com/hortonworks/lsr.php?RCID=de6d0c435c0761adedf3114a100e7483%20
Big data spain keynote nov 2016
Keynote slides from Big Data Spain Nov 2016. Has some thoughts on how Hadoop ecosystem is growing and changing to support the enterprise, including Hive, Spark, NiFi, security and governance, streaming, and the cloud.
The Enterprise and Connected Data, Trends in the Apache Hadoop Ecosystem by A...
https://www.bigdataspain.org/2016/program/thu-enterprise-connected-data-trends-apache-hadoop-ecosystem.html
https://www.youtube.com/watch?v=WArxOEmtv1s&index=5&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&t=52s
Apache Phoenix Query Server PhoenixCon2016
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Intro to Big Data Analytics using Apache Spark and Apache Zeppelin
This workshop will provide an introduction to Big Data Analytics using Apache Spark and Apache Zeppelin.
https://github.com/zeltovhorton/intro_spark_zeppelin_meetup
There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
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An Apache Hive Based Data Warehouse
Using Apache Hadoop and related technologies as a data warehouse has been an area of interest since the early days of Hadoop. In recent years Hive has made great strides towards enabling data warehousing by expanding its SQL coverage, adding transactions, and enabling sub-second queries with LLAP. But data warehousing requires more than a full powered SQL engine. Security, governance, data movement, workload management, monitoring, and user tools are required as well. These functions are being addressed by other Apache projects such as Ranger, Atlas, Falcon, Ambari, and Zeppelin. This talk will examine how these projects can be assembled to build a data warehousing solution. It will also discuss features and performance work going on in Hive and the other projects that will enable more data warehousing use cases. These include use cases like data ingestion using merge, support for OLAP cubing queries via Hive’s integration with Druid, expanded SQL coverage, replication of data between data warehouses, advanced access control options, data discovery, and user tools to manage, monitor, and query the warehouse.
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache phoenix: Past, Present and Future of SQL over HBAse
HBase as the NoSQL database of choice in the Hadoop ecosystem has already been proven itself in scale and in many mission critical workloads in hundreds of companies. Phoenix as the SQL layer on top of HBase, has been increasingly becoming the tool of choice as the perfect complementary for HBase. Phoenix is now being used more and more for super low latency querying and fast analytics across a large number of users in production deployments. In this talk, we will cover what makes Phoenix attractive among current and prospective HBase users, like SQL support, JDBC, data modeling, secondary indexing, UDFs, and also go over recent improvements like Query Server, ODBC drivers, ACID transactions, Spark integration, etc. We will conclude by looking into items in the pipeline and how Phoenix and HBase interacts with other engines like Hive and Spark.
Apache Phoenix and HBase - Hadoop Summit Tokyo, Japan
Apache Phoenix is an open source, massively parallel, relational database engine supporting OLTP for Hadoop using Apache HBase as its backing store.
How YARN Enables Multiple Data Processing Engines in Hadoop
Eric Mizell
Hortonworks - Director of Solution Engineering
POSSCON 2015
4/15/2015
Big Data/Cloud Track - 9:45 AM Workshop
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Intro to Big Data Analytics using Apache Spark and Apache Zeppelin
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How YARN Enables Multiple Data Processing Engines in Hadoop
More from DataWorks Summit
Data Science Crash Course
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 Ratis
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 NiFi
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...
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...
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.
Managing the Dewey Decimal System
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.
Practical NoSQL: Accumulo's dirlist Example
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 Uber
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 Phoenix
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 NiFi
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.
Supporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability Improvements
Security Framework for Multitenant Architecture
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 Engine
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...
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 Cloud
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 NiFi
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 Ranger
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...
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.
Computer Vision: Coming to a Store Near You
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 Spark
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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1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Key Trends Shaping the Future of Infrastructure.pdf
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
The Art of the Pitch: WordPress Relationships and Sales
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
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JMeter webinar - integration with InfluxDB and Grafana
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
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- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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Epistemic Interaction - tuning interfaces to provide information for AI support
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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Connector Corner: Automate dynamic content and events by pushing a button
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
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But there’s more:
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Bob Boule
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Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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State of ICS and IoT Cyber Threat Landscape Report 2024 preview
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
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Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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Meet HBase 2.0 and Phoenix 5.0
- 1. 1 © Hortonworks Inc. 2011–2018. All rights reserved Apache HBase and Apache Phoenix Alan Gates Co-founder Hortonworks @alanfgates
- 2. 2 © Hortonworks Inc. 2011–2018. All rights reserved Phoenix and HBase provides Big Data OLTP solution Flexible Schema Columnar Storage Optimal for Reads by Key Insert, Update, Delete Millisecond Latency Strong Consistency SQL and NoSQL Interfaces Flexible Schema Extreme Low Latency Directly Integrated with Hadoop SQL and NoSQL Interfaces YARN : Data Operating System HBase RegionServer 1 ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° N HDFS (Permanent Data Storage) HBase RegionServer HBase RegionServer
- 3. 3 © Hortonworks Inc. 2011–2018. All rights reserved HBase: Fast Facts Largest Database 5 Petabytes (Flurry) Best Known App Facebook Messages (Facebook) Fastest Ingestion 25 Million Events/s (Alibaba) Biggest SQL App Real-Time SQL on 200m+ Records (PubMatic)
- 4. 4 © Hortonworks Inc. 2011–2018. All rights reserved Partition Tolerant Consistent Available CP AP CA MongoDB HBase Redis Cassandra CouchBase Dynamo Riak MySQL Postgres CAP theorem
- 5. 5 © Hortonworks Inc. 2011–2018. All rights reserved
- 6. 6 © Hortonworks Inc. 2011–2018. All rights reserved New Features in HBase 2
- 7. 7 © Hortonworks Inc. 2011–2018. All rights reserved HBase 2.0 off-heap read 4-7x decrease in data access latency Up to 2x increase in throughput
- 8. 8 © Hortonworks Inc. 2011–2018. All rights reserved HBase 2: Compacting memstore (aka. Accordion) • In memory compaction • In memory index reduction • Reduce flushes to disk • 30% reduction of write amplification • 20% increase of write throughput • 22% reduction of GC https://blogs.apache.org/hbase/entry/accordion-hbase-breathes-with-in
- 9. 9 © Hortonworks Inc. 2011–2018. All rights reserved Apache Phoenix Overview Apache Phoenix: • Originally from Salesforce to offload Oracle workloads • ANSI SQL (SQL-92+) • Upsert for easy ingestion from streams • Millisecond Latency • Secondary Indexes • Develop in JDBC, ODBC, .NET, Python and more • Standalone or on top of existing HBase apps
- 10. 10 © Hortonworks Inc. 2011–2018. All rights reserved Flexible schemas for evolving use-cases HBase/Phoenix as the data store for mutable data ● HBase provides low-level, key-value store semantics ● Phoenix provides usability and accessibility ● Use the right tool and optimally-structured data ● Dynamic schema benefits on read and write
- 11. 11 © Hortonworks Inc. 2011–2018. All rights reserved BI Tools connectivity ⬢ Plugs into any ODBC-compatible BI tool ⬢ Phoenix layers natively on existing HBase applications for ad-hoc SQL analytics on Phoenix or HBase applications ⬢ Self-service analytics and insight for The Hadoop Database Ad-Hoc Analytics for Phoenix or HBase Phoenix or HBase Applications
- 12. 12 © Hortonworks Inc. 2011–2018. All rights reserved Phoenix Query Server Native Interfaces (DB-API, DBI, etc.) Applications within the Datacenter Phoenix Query Server HBase RegionServer HBase RegionServer HBase RegionServer Phoenix Query Server Phoenix Query Server Protobuf over HTTP Phoenix Query Server Edge Servers Business Analysts + Standard SQL Tools
- 13. 13 © Hortonworks Inc. 2011–2018. All rights reserved New Features in Phoenix
- 14. 14 © Hortonworks Inc. 2011–2018. All rights reserved HBase and Phoenix Connectors GA ✗ Tech-preview GA (RDD and DF)Tech-preview GA in 3.0.x (DF only)
- 15. 15 © Hortonworks Inc. 2011–2018. All rights reserved Phoenix Kafka Consumer • Users often connect Kafka to HBase • As of Phoenix 4.10, can now do it in SQL using Phoenix serializer.columns=c1,c2,c3 jdbcUrl=jdbc:phoenix:localhost table=SAMPLE2 ddl=CREATE TABLE IF NOT EXISTS SAMPLE2(uid VARCHAR NOT NULL,c1 VARCHAR,c2 VARCHAR,c3 VARCHAR CONSTRAINT pk PRIMARY KEY(uid)) topics=topic1,topic2 ... Kafka-consumer-json.properties: hadoop jar phoenix-kafka-minimal.jar org.apache.phoenix.kafka.consumer.PhoenixConsumerTool --file /data/kafka-consumer-json.properties Start the consumer:
- 16. 16 © Hortonworks Inc. 2011–2018. All rights reserved SQL Cursors PreparedStatement statement = conn.prepareStatement( "DECLARE empCursor CURSOR FOR SELECT * FROM EMP_TABLE"); statement.execute(); statement = con.prepareStatement("OPEN empCursor"); statement.execute(); statement = con.prepareStatement("FETCH NEXT 10 ROWS FROM empCursor"); ResultSet rset = statement.execute(); while (rset.next() != null){ ... }
- 17. 17 © Hortonworks Inc. 2011–2018. All rights reserved Repeatable Bernoulli Sampling • TABLESAMPLE function allows only a portion of table to be read • Stats include marker rows at intervals in the table • TABLESAMPLE argument indicates probably of any given marker being chosen, all rows from chosen marker to next marker read • Deterministic function used to select markers, so select returns consistent results across reads SELECT * from PERSON TABLESAMPLE(10);
- 18. 18 © Hortonworks Inc. 2011–2018. All rights reserved Plus more • Optimize DISTINCT queries by pushing down seek logic to server • Atomic UPSERT through new ON DUPLICATE KEY syntax • Support Apache Spark 2.0 in Phoenix/Spark integration • SELECT APPROX_COUNT_DISTINCT(x) – Uses hyperloglog, much faster than regular count distinct