HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Speaker: Alan Gates, Co-Founder, Hortonworks
Apache Hadoop YARN is the latest distributed operating system for HDSF for big data applications and storage. YARN has transformed the Hadoop Compute Layer into a general resource management platform capable of hosting a wide variety of applications.
This lecture begins with the current state how Apache Hadoop YARN is currently used in large scale deployment. The next topic will cover about strengthening YARN 's current and future - like YARN' s excitement - as a top-notch resource management platform for data centers running enterprise Hadoop. Discuss the current state and future of the following functions and initiatives: support of machine learning through strong container placement, global scheduling, GPU and FPGA support and deep learning workload, large scale of YARN federation, on YARN Containerized applications, natural support that does not change to long-running services (along with applications), seamless application upgrades, powerful scheduling functions, operational improvements and better queue management.
The second part of the lecture focuses on the latest enhancement of HDFS. HDFS has several advantages: horizontal scale of IO bandwidth, storage scaled to petabyte storage. In addition, it provides extremely low latency metadata operations and coordinates for over 60,000 concurrent clients. Hadoop 3.0 recently introduced Erasure Coding. One limitation of HDFS is the scaling of multiple files and blocks in the system. I will explain the fundamental change of Hadoop's storage infrastructure using Ozone technology which will be announced soon. This will allow Hadoop to scale billions of files and blocks in the future to a larger number of smaller objects than before.
Apache Spark 2.0 set the architectural foundations of structure in Spark, unified high-level APIs, structured streaming, and the underlying performant components like Catalyst Optimizer and Tungsten Engine. Since then the Spark community has continued to build new features and fix numerous issues in releases Spark 2.1 and 2.2.
Apache Spark 2.3 & 2.4 has made similar strides too. In this talk, we want to highlight some of the new features and enhancements, such as:
• Apache Spark and Kubernetes
• Native Vectorized ORC and SQL Cache Readers
• Pandas UDFs for PySpark
• Continuous Stream Processing
• Barrier Execution
• Avro/Image Data Source
• Higher-order Functions
Speaker: Robert Hryniewicz, AI Evangelist, Hortonworks
Using Spark Streaming and NiFi for the Next Generation of ETL in the EnterpriseDataWorks Summit
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
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
The First Mile – Edge and IoT Data Collection with Apache NiFi and MiNiFiDataWorks Summit
Apache NiFi MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately.
Abstract: Apache NiFi provided a revolutionary data flow management system with a broad range of integrations with existing data production, consumption, and analysis ecosystems, all covered with robust data delivery and provenance infrastructure. Now learn about the follow-on project which expands the reach of NiFi to the edge, Apache MiNiFi. MiNiFi is a lightweight application which can be deployed on hardware orders of magnitude smaller and less powerful than the existing standard data collection platforms. With both a JVM compatible and native agent, MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately. Local governance and regulatory policies can be applied across geopolitical boundaries to conform with legal requirements. And all of this configuration can be done from central command & control using an existing NiFi with the trusted and stable UI data flow managers already love.
Expected prior knowledge / intended audience: developers and data flow managers should have a passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The talk will focus on extending the data collection, routing, provenance, and governance capabilities of NiFi to IoT/edge integration via MiNiFi.
Takeaways: Attendees will learn about opportunities to bring their data flow and capture closer to the "edge" -- sources of data like IoT devices, vehicles, machinery, etc. They will understand the possibilities to prioritize, filter, secure, and manipulate this data earlier in the data lifecycle to enhance their data visibility and performance.
Speaker: Andy LoPresto, Sr. Member of Technical Staff, Hortonworks
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.
Dataflow Management From Edge to Core with Apache NiFiDataWorks Summit
What is “dataflow?” — the process and tooling around gathering necessary information and getting it into a useful form to make insights available. Dataflow needs change rapidly — what was noise yesterday may be crucial data today, an API endpoint changes, or a service switches from producing CSV to JSON or Avro. In addition, developers may need to design a flow in a sandbox and deploy to QA or production — and those database passwords aren’t the same (hopefully). Learn about Apache NiFi — a robust and secure framework for dataflow development and monitoring.
Abstract: Identifying, collecting, securing, filtering, prioritizing, transforming, and transporting abstract data is a challenge faced by every organization. Apache NiFi and MiNiFi allow developers to create and refine dataflows with ease and ensure that their critical content is routed, transformed, validated, and delivered across global networks. Learn how the framework enables rapid development of flows, live monitoring and auditing, data protection and sharing. From IoT and machine interaction to log collection, NiFi can scale to meet the needs of your organization. Able to handle both small event messages and “big data” on the scale of terabytes per day, NiFi will provide a platform which lets both engineers and non-technical domain experts collaborate to solve the ingest and storage problems that have plagued enterprises.
Expected prior knowledge / intended audience: developers and data flow managers should be interested in learning about and improving their dataflow problems. The intended audience does not need experience in designing and modifying data flows.
Takeaways: Attendees will gain an understanding of dataflow concepts, data management processes, and flow management (including versioning, rollbacks, promotion between deployment environments, and various backing implementations).
Current uses: I am a committer and PMC member for the Apache NiFi, MiNiFi, and NiFi Registry projects and help numerous users deploy these tools to collect data from an incredibly diverse array of endpoints, aggregate, prioritize, filter, transform, and secure this data, and generate actionable insight from it. Current users of these platforms include many Fortune 100 companies, governments, startups, and individual users across fields like telecommunications, finance, healthcare, automotive, aerospace, and oil & gas, with use cases like fraud detection, logistics management, supply chain management, machine learning, IoT gateway, connected vehicles, smart grids, etc.
With the rise of IoT and the increasing complexity of applications, clouds, networks and infrastructure, the battle to keep your data and your infrastructure safe from attackers is getting harder. As groups of bad actors collaborate, sharing information and offering illegal access, and botnets as a service, terabits of attack can be launched cheaply. Meanwhile, it’s hard to find enough security analysts to catch and prevent these attacks.
This is where community collaboration and open source efforts like Apache Metron come in. Metron presents a comprehensive framework for application and network, security built on Apache Hadoop and open source Streaming Analytics(ie Apache Nifi, Apache Kafka) tool’s highly scalable data management and processing stacks. Advanced features like profiling, machine learning, and visualization work with real-time streaming detection to make your SOC analysts more efficient, while the intrinsic extensibility of open source helps your data scientists get security insights out of the lab and into production fast.
We will discuss and demonstrate how some real-world businesses and managed service providers are using Apache Metron to identify and solve security threats at scale, and some approaches and ideas for how the platform can fit into your security architecture.
Speaker: Laurence Da Luz, Senior Solutions Architect, Hortonworks
Apache Spark 2.0 set the architectural foundations of structure in Spark, unified high-level APIs, structured streaming, and the underlying performant components like Catalyst Optimizer and Tungsten Engine. Since then the Spark community has continued to build new features and fix numerous issues in releases Spark 2.1 and 2.2.
Apache Spark 2.3 has made similar strides too, introducing new features and resolving over 1300 JIRA issues. Likewise, Apache Spark 2.4 will have many JIRA issues resolved over 1100. In this talk, I want to skim and go through those notable features and changes.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
How to Ingest 16 Billion Records Per Day into your Hadoop EnvironmentDataWorks Summit
In a modern society, mobile networks has become one of the most important infrastructure components. The availability of a mobile network has become even essential in areas like health care and machine to machine communication.
In 2016, Telefónica Germany begun the Customer Experience Management (CEM) project to get KPI out of the mobile network describing the participant’s experience while using the Telefónica’s mobile network. These KPI help to plan and create a better mobile network where improvements are indicated.
Telefónica is using Hortonworks HDF solution to ingest 16 billion records a day which are generated by CEM. To achieve the best out of HDF abilities some customizations have been made:
1.) Custom processors have been written to comply with data privacy rules.
2.) Nifi is running in Docker containers within a Kubernetes cluster to increase reliability of the ingestion system.
Finally, the data is presented in Hive tables and Kafka topics to be further processed. In this talk, we will present the CEM use case and how it is technically implemented as stated in (1) and (2). Most interesting part for the audience should be our experiences we have made using HDF in a Docker/Kubernetes environment since this solution is not yet officially supported.
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Curing the Kafka Blindness – Streams Messaging ManagerDataWorks Summit
Companies who use Kafka today struggle with monitoring and managing Kafka clusters. Kafka is a key backbone of IoT streaming analytics applications. The challenge is understanding what is going on overall in the Kafka cluster including performance, issues and message flows. No open source tool caters to the needs of different users that work with Kafka: DevOps/developers, platform team, and security/governance teams. See how the new Hortonworks Streams Messaging Manager enables users to visualize their entire Kafka environment end-to-end and simplifies Kafka operations.
In this session learn how SMM visualizes the intricate details of how Apache Kafka functions in real time while simultaneously surfacing every nuance of tuning, optimizing, and measuring input and output. SMM will assist users to quickly understand and operate Kafka while providing the much-needed transparency that sophisticated and experienced users need to avoid all the pitfalls of running a Kafka cluster.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
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
Santhosh B Gowda presents on Cloudbreak, a tool for provisioning Hadoop clusters on cloud infrastructure. Cloudbreak allows for simplified cluster provisioning through prescriptive setups and automation. It supports declarative workload provisioning across multiple cloud providers with flexible topologies and security configuration options. Cloudbreak also enables features like auto-scaling, recipes to customize clusters, and shared services data lakes to provide common metadata and access management across ephemeral clusters. Demonstrations of launching HDP and HDF clusters from the Cloudbreak UI and CLI are also provided.
The document provides an overview of the state of Apache Hadoop YARN. Key themes discussed include scaling to support very large clusters of 100,000+ nodes, improved global and fast scheduling capabilities, richer placement constraints, and enhanced support for containers, resources like GPUs and FPGAs, and services. The YARN community continues to grow with over 450 contributors.
While developing on Apache NiFi MiNiFI C++, my focus has been writing architectural details from Apache NiFi in C++. During this time I've learned of the myriad of applications in which IoT exists. In this presentation we'll discuss key lessons I've learned along with ways that we can help you navigate the IoT realm from the perspective of technical challenges that you will face.
We will begin with an overview of Apache NiFi MiNiFi C++ followed by discussions of system awareness. In doing so we'll focus on system monitoring for battery and network status. We'll look at how MiNIFi can be configured to monitor battery and network status to throttle itself and be a good steward of device management. In the same vein we'll discuss the idiosyncrasies that you must be aware of when dealing with common IoT use cases, such as connected cars and embedded devices on the move. Finally, we'll look at lessons we've learned in helping other developers write software in the IoT realm. MARC PARISI, Principal SW Engineer, Hortonworks
The document discusses scaling challenges with HDFS and proposed solutions from Hortonworks called HDDS and Ozone. HDFS scales well for data and IO but has limitations scaling the namespace beyond 500 million files. HDDS aims to scale the block layer using block containers which can reduce block reports. Ozone uses a flat key-value namespace that is easier to shard and scale beyond billions of objects compared to HDFS hierarchical namespace. It also provides an HDFS compatible filesystem called OzoneFS. Together HDDS and Ozone aim to retain HDFS features while scaling to exabytes of data and trillions of files.
Sanjay Radia presents on evolving HDFS to support a generalized storage subsystem. HDFS currently scales well to large clusters and storage sizes but faces challenges with small files and blocks. The solution is to (1) only keep part of the namespace in memory to scale beyond memory limits and (2) use block containers of 2-16GB to reduce block metadata and improve scaling. This will generalize the storage layer to support containers for multiple use cases beyond HDFS blocks.
The document discusses evolving HDFS to better support large scale deployments. It summarizes HDFS's strengths in scaling to large clusters and data sizes. However, scaling the large number of small files and blocks is challenging. The solution involves using partial namespaces to store only recently used metadata in memory, and block containers to group blocks together. This will generalize the storage layer to support different container types beyond HDFS blocks. Initial goals are to scale to billions of files and blocks per volume, with the ability to add more volumes for further scaling. The changes will also enable new use cases like block storage and caching data in cloud storage.
The document discusses evolving HDFS to support generalized storage containers in order to better scale the number of files and blocks. It proposes using block containers and a partial namespace approach to initially scale to billions of files and blocks, and eventually much higher numbers. The storage layer is being restructured to support various container types for use cases beyond HDFS like object storage and HBase.
Ozone is an object store for Hadoop. Ozone solves the small file problem of HDFS, which allows users to store trillions of files in Ozone and access them as if there are on HDFS. Ozone plugs into existing Hadoop deployments seamlessly, and programs like Hive, LLAP, and Spark work without any modifications. This talk looks at the architecture, reliability, and performance of Ozone.
In this talk, we will also explore Hadoop distributed storage layer, a block storage layer that makes this scaling possible, and how we plan to use the Hadoop distributed storage layer for scaling HDFS.
We will demonstrate how to install an Ozone cluster, how to create volumes, buckets, and keys, how to run Hive and Spark against HDFS and Ozone file systems using federation, so that users don’t have to worry about where the data is stored. In other words, a full user primer on Ozone will be part of this talk.
Speakers
Anu Engineer, Software Engineer, Hortonworks
Xiaoyu Yao, Software Engineer, Hortonworks
Ozone is an object store that can be built into HDFS to provide highly scalable object storage capabilities. It uses a hashing algorithm to map object keys to storage containers, which are then distributed across data nodes similarly to HDFS blocks. The storage containers are managed by a storage container manager that maintains metadata about container locations and performs functions like replication. This allows Ozone to provide secure, reliable storage of trillions of objects with a wide range of sizes.
The current major release, Hadoop 2.0 offers several significant HDFS improvements including new append-pipeline, federation, wire compatibility, NameNode HA, Snapshots, and performance improvements. We describe how to take advantages of these new features and their benefits. We cover some architectural improvements in detail such as HA, Federation and Snapshots. The second half of the talk describes the current features that are under development for the next HDFS release. This includes much needed data management features such as backup and Disaster Recovery. We add support for different classes of storage devices such as SSDs and open interfaces such as NFS; together these extend HDFS as a more general storage system. Hadoop has recently been extended to run first-class on Windows which expands its enterprise reach and allows integration with the rich tool-set available on Windows. As with every release we will continue improvements to performance, diagnosability and manageability of HDFS. To conclude, we discuss the reliability, the state of HDFS adoption, and some of the misconceptions and myths about HDFS.
Hadoop Distributed File System (HDFS) evolves from a MapReduce-centric storage system to a generic, cost-effective storage infrastructure where HDFS stores all data of inside the organizations. The new use case presents a new sets of challenges to the original HDFS architecture. One challenge is to scale the storage management of HDFS - the centralized scheme within NameNode becomes a main bottleneck which limits the total number of files stored. Although a typical large HDFS cluster is able to store several hundred petabytes of data, it is inefficient to handle large amounts of small files under the current architecture.
In this talk, we introduce our new design and in-progress work that re-architects HDFS to attack this limitation. The storage management is enhanced to a distributed scheme. A new concept of storage container is introduced for storing objects. HDFS blocks are stored and managed as objects in the storage containers instead of being tracked only by NameNode. Storage containers are replicated across DataNodes using a newly-developed high-throughput protocol based on the Raft consensus algorithm. Our current prototype shows that under the new architecture the storage management of HDFS scales 10x better, demonstrating that HDFS is capable of storing billions of files.
Ozone is an object store for Apache Hadoop that is designed to scale to trillions of objects. It uses a distributed metadata store to avoid single points of failure and enable parallelism. Key components of Ozone include containers, which provide the basic storage and replication functionality, and the Key Space Manager (KSM) which maps Ozone entities like volumes and buckets to containers. The Storage Container Manager manages the container lifecycle and replication.
Scaling HDFS to Manage Billions of Files with Distributed Storage SchemesDataWorks Summit
The document discusses scaling HDFS to manage billions of files through distributed storage schemes. It outlines the current HDFS architecture and challenges with namespace and block scaling. It proposes a storage container architecture with distributed block maps and a storage container manager to address these challenges. This would allow HDFS to easily scale to manage trillions of blocks and billions of files across large clusters.
HBase and HDFS: Understanding FileSystem Usage in HBaseenissoz
This document discusses file system usage in HBase. It provides an overview of the three main file types in HBase: write-ahead logs (WALs), data files, and reference files. It describes durability semantics, IO fencing techniques for region server recovery, and how HBase leverages data locality through short circuit reads, checksums, and block placement hints. The document is intended help understand HBase's interactions with HDFS for tuning IO performance.
HBaseCon 2013: Apache HBase and HDFS - Understanding Filesystem Usage in HBaseCloudera, Inc.
This document discusses file system usage in HBase. It describes the main file types in HBase including write ahead logs (WALs), data files, and reference files. It covers topics like durability semantics, IO fencing, and data locality techniques used in HBase like short circuit reads, checksums, and block placement. The document is presented by Enis Söztutar and is intended to help understand how HBase performs IO operations over HDFS for tuning performance.
Apache Hadoop 3.0 is coming! As the next major release, it attracts everyone's attention as show case several bleeding-edge technologies and significant features across all components of Apache Hadoop, include: Erasure Coding in HDFS, Multiple Standby NameNodes, YARN Timeline Service v2, JNI-based shuffle in MapReduce, Apache Slider integration and Service Support as First Class Citizen, Hadoop library updates and client-side class path isolation, etc.
In this talk, we will update the status of Hadoop 3 especially the releasing work in community and then go deep diving on new features included in Hadoop 3.0. As a new major release, Hadoop 3 would also include some incompatible changes - we will go through most of these changes and explore its impact to existing Hadoop users and operators. In the last part of this session, we will continue to discuss ongoing efforts in Hadoop 3 age and show the big picture that how big data landscape could be largely influenced by Hadoop 3.
Apache Hadoop 3 is coming! As the next major milestone for hadoop and big data, it attracts everyone's attention as showcase several bleeding-edge technologies and significant features across all components of Apache Hadoop: Erasure Coding in HDFS, Docker container support, Apache Slider integration and Native service support, Application Timeline Service version 2, Hadoop library updates and client-side class path isolation, etc. In this talk, first we will update the status of Hadoop 3.0 releasing work in apache community and the feasible path through alpha, beta towards GA. Then we will go deep diving on each new feature, include: development progress and maturity status in Hadoop 3. Last but not the least, as a new major release, Hadoop 3.0 will contain some incompatible API or CLI changes which could be challengeable for downstream projects and existing Hadoop users for upgrade - we will go through these major changes and explore its impact to other projects and users.
Speaker: Sanjay Radia, Founder and Chief Architect, Hortonworks
The document discusses improvements to HDFS that allow it to leverage memory as a storage medium. Key points include:
- HDFS 2.3 introduced memory as a storage medium, with RAM disks providing persistence across restarts.
- HDFS 2.6 introduced storage policies that allow applications to target different storage media like SSD or memory.
- The Centralized Cache Management feature loads hot data into memory pools to enable zero-copy reads.
- The Lazy Persist Writes feature allows applications to write to memory and have HDFS asynchronously write to persistent storage, reducing latency.
- Future work includes improving caching, short-circuit writes, and the Memfs layered file system to provide more flexible
You’ve successfully deployed Hadoop, but are you taking advantage of all of Hadoop’s features to operate a stable and effective cluster? In the first part of the talk, we will cover issues that have been seen over the last two years on hundreds of production clusters with detailed breakdown covering the number of occurrences, severity, and root cause. We will cover best practices and many new tools and features in Hadoop added over the last year to help system administrators monitor, diagnose and address such incidents.
The second part of our talk discusses new features for making daily operations easier. This includes features such as ACLs for simplified permission control, snapshots for data protection and more. We will also cover tuning configuration and features that improve cluster utilization, such as short-circuit reads and datanode caching.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
Dancing elephants - efficiently working with object stores from Apache Spark ...DataWorks Summit
As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different,
What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them.
We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you.
This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.
Big Data and Hadoop - History, Technical Deep Dive, and Industry TrendsEsther Kundin
An overview of the history of Big Data, followed by a deep dive into the Hadoop ecosystem. Detailed explanation of how HDFS, MapReduce, and HBase work, followed by a discussion of how to tune HBase performance. Finally, a look at industry trends, including challenges faced and being solved by Bloomberg for using Hadoop for financial data.
Hadoop Present - Open Enterprise HadoopYifeng Jiang
The document is a presentation on enterprise Hadoop given by Yifeng Jiang, a Solutions Engineer at Hortonworks. The presentation covers updates to Hadoop Core including HDFS and YARN, data access technologies like Hive, Spark and stream processing, security features in Hadoop, and Hadoop management with Apache Ambari.
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Danny Chen presented on Uber's use of HBase for global indexing to support large-scale data ingestion. Uber uses HBase to provide a global view of datasets ingested from Kafka and other data sources. To generate indexes, Spark jobs are used to transform data into HFiles, which are loaded into HBase tables. Given the large volumes of data, techniques like throttling HBase access and explicit serialization are used. The global indexing solution supports requirements for high throughput, strong consistency and horizontal scalability across Uber's data lake.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
This document discusses using Apache NiFi to build a high-speed cyber security data pipeline. It outlines the challenges of ingesting, transforming, and routing large volumes of security data from various sources to stakeholders like security operations centers, data scientists, and executives. It proposes using NiFi as a centralized data gateway to ingest data from multiple sources using a single entry point, transform the data according to destination needs, and reliably deliver the data while avoiding issues like network traffic and data duplication. The document provides an example NiFi flow and discusses metrics from processing over 20 billion events through 100+ production flows and 1000+ transformations.
Supporting Apache HBase : Troubleshooting and Supportability ImprovementsDataWorks Summit
This document discusses supporting Apache HBase and improving troubleshooting and supportability. It introduces two Cloudera employees who work on HBase support and provides an overview of typical troubleshooting scenarios for HBase like performance degradation, process crashes, and inconsistencies. The agenda covers using existing tools like logs and metrics to troubleshoot HBase performance issues with a general approach, and introduces htop as a real-time monitoring tool for HBase.
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
TrustArc Webinar - Innovating with TRUSTe Responsible AI CertificationTrustArc
In a landmark year marked by significant AI advancements, it’s vital to prioritize transparency, accountability, and respect for privacy rights with your AI innovation.
Learn how to navigate the shifting AI landscape with our innovative solution TRUSTe Responsible AI Certification, the first AI certification designed for data protection and privacy. Crafted by a team with 10,000+ privacy certifications issued, this framework integrated industry standards and laws for responsible AI governance.
This webinar will review:
- How compliance can play a role in the development and deployment of AI systems
- How to model trust and transparency across products and services
- How to save time and work smarter in understanding regulatory obligations, including AI
- How to operationalize and deploy AI governance best practices in your organization
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
"Building Future-Ready Apps with .NET 8 and Azure Serverless Ecosystem", Stan...Fwdays
.NET 8 brought a lot of improvements for developers and maturity to the Azure serverless container ecosystem. So, this talk will cover these changes and explain how you can apply them to your projects. Another reason for this talk is the re-invention of Serverless from a DevOps perspective as a Platform Engineering trend with Backstage and the recent Radius project from Microsoft. So now is the perfect time to look at developer productivity tooling and serverless apps from Microsoft's perspective.
Generative AI technology is a fascinating field that focuses on creating comp...Nohoax Kanont
Generative AI technology is a fascinating field that focuses on creating computer models capable of generating new, original content. It leverages the power of large language models, neural networks, and machine learning to produce content that can mimic human creativity. This technology has seen a surge in innovation and adoption since the introduction of ChatGPT in 2022, leading to significant productivity benefits across various industries. With its ability to generate text, images, video, and audio, generative AI is transforming how we interact with technology and the types of tasks that can be automated.
Demystifying Neural Networks And Building Cybersecurity ApplicationsPriyanka Aash
In today's rapidly evolving technological landscape, Artificial Neural Networks (ANNs) have emerged as a cornerstone of artificial intelligence, revolutionizing various fields including cybersecurity. Inspired by the intricacies of the human brain, ANNs have a rich history and a complex structure that enables them to learn and make decisions. This blog aims to unravel the mysteries of neural networks, explore their mathematical foundations, and demonstrate their practical applications, particularly in building robust malware detection systems using Convolutional Neural Networks (CNNs).
Cracking AI Black Box - Strategies for Customer-centric Enterprise ExcellenceQuentin Reul
The democratization of Generative AI is ushering in a new era of innovation for enterprises. Discover how you can harness this powerful technology to deliver unparalleled customer value and securing a formidable competitive advantage in today's competitive market. In this session, you will learn how to:
- Identify high-impact customer needs with precision
- Harness the power of large language models to address specific customer needs effectively
- Implement AI responsibly to build trust and foster strong customer relationships
Whether you're at the early stages of your AI journey or looking to optimize existing initiatives, this session will provide you with actionable insights and strategies needed to leverage AI as a powerful catalyst for customer-driven enterprise success.
"Hands-on development experience using wasm Blazor", Furdak Vladyslav.pptxFwdays
I will share my personal experience of full-time development on wasm Blazor
What difficulties our team faced: life hacks with Blazor app routing, whether it is necessary to write JavaScript, which technology stack and architectural patterns we chose
What conclusions we made and what mistakes we committed
Keynote : AI & Future Of Offensive SecurityPriyanka Aash
In the presentation, the focus is on the transformative impact of artificial intelligence (AI) in cybersecurity, particularly in the context of malware generation and adversarial attacks. AI promises to revolutionize the field by enabling scalable solutions to historically challenging problems such as continuous threat simulation, autonomous attack path generation, and the creation of sophisticated attack payloads. The discussions underscore how AI-powered tools like AI-based penetration testing can outpace traditional methods, enhancing security posture by efficiently identifying and mitigating vulnerabilities across complex attack surfaces. The use of AI in red teaming further amplifies these capabilities, allowing organizations to validate security controls effectively against diverse adversarial scenarios. These advancements not only streamline testing processes but also bolster defense strategies, ensuring readiness against evolving cyber threats.
Retrieval Augmented Generation Evaluation with RagasZilliz
Retrieval Augmented Generation (RAG) enhances chatbots by incorporating custom data in the prompt. Using large language models (LLMs) as judge has gained prominence in modern RAG systems. This talk will demo Ragas, an open-source automation tool for RAG evaluations. Christy will talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.
This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
Increase Quality with User Access Policies - July 2024Peter Caitens
⭐️ Increase Quality with User Access Policies ⭐️, presented by Peter Caitens and Adam Best of Salesforce. View the slides from this session to hear all about “User Access Policies” and how they can help you onboard users faster with greater quality.
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.