The document discusses Kudu, a new updatable columnar storage system for Hadoop that was built to address gaps in transactional and analytic capabilities of existing Hadoop storage technologies like HDFS and HBase. Kudu aims to provide both high throughput for large scans like HDFS and low latency for individual row lookups and updates like HBase, while supporting SQL queries and a relational data model. It leverages improvements in hardware by using a columnar format and indexes to improve CPU efficiency for these workloads compared to traditional storage systems. The document outlines Kudu's goals and capabilities and provides examples of use cases like time series analytics, machine data analytics and online reporting that would benefit from Kudu's simultaneous support for sequential
Capital One's Next Generation Decision in less than 2 msApache Apex
This document discusses using Apache Apex for real-time decision making within 2 milliseconds. It provides performance benchmarks for Apex, showing average latency of 0.25ms for over 54 million events with 600GB of RAM. It compares Apex favorably to other streaming technologies like Storm and Flink, noting Apex's self-healing capabilities, independence of operators, and ability to meet latency and throughput requirements even during failures. The document recommends Apex for its maturity, fault tolerance, and ability to meet the goals of latency under 16ms, 99.999% availability, and scalability.
The document discusses in-flux limiting for a multi-tenant logging service. It describes Symantec's logging and metrics architecture using Kafka, Elasticsearch, and InfluxDB. It addresses the issue of ingestion spikes overwhelming InfluxDB and presents a solution to normalize event rates using buffers that allocate ingestion quotas per tenant. The design implements rate limiting using a scheduled task pattern in Storm to track each tenant's event rate over a configurable window and throttle events if the threshold is exceeded.
This document discusses the performance metrics and capabilities of an enterprise grade streaming platform called Onyx. It can process streaming data with latencies under 2ms on Hadoop clusters. The key metrics it aims for are latencies under 16ms, throughput of 2000 events/second, 99.5% uptime, and the ability to scale resources while maintaining latency. It also aims to have open source components, extensible rules, and transparent integration with existing systems. Testing showed it can process over 70,000 records/second with average latency of 0.19ms and meet stringent reliability targets.
Spark-on-Yarn: The Road Ahead-(Marcelo Vanzin, Cloudera)Spark Summit
Spark on YARN provides resource management and security features through YARN, but still has areas for improvement. Dynamic allocation in YARN allows Spark applications to grow and shrink executors based on task demand, though latency and data locality could be enhanced. Security supports Kerberos authentication and delegation tokens, but long-lived applications face token expiration issues and encryption needs improvement for control plane, shuffle files, and user interfaces. Overall, usability, security, and performance remain areas of focus.
Ingestion and Dimensions Compute and Enrich using Apache ApexApache Apex
Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms.
This talk will be a deep dive into ingesting unbounded file data and streaming data from Kafka into Hadoop. We will also cover data enrichment and dimensional compute. Customer use-case and reference architecture.
Spark is a fast and general engine for large-scale data processing. It provides APIs in Java, Scala, and Python and an interactive shell. Spark applications operate on resilient distributed datasets (RDDs) that can be cached in memory for faster performance. RDDs are immutable and fault-tolerant via lineage graphs. Transformations create new RDDs from existing ones while actions return values to the driver program. Spark's execution model involves a driver program that coordinates tasks on executor machines. RDD caching and lineage graphs allow Spark to efficiently run jobs across clusters.
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...Data Con LA
While the last few years have seen great advancements in computing paradigms for big data stores, there remains one critical bottleneck in this architecture - the ingestion process. Instead of immediate insights into the data, a poor ingestion process can cause headaches and problems to no end. On the other hand, a well-designed ingestion infrastructure should give you real-time visibility into how your systems are functioning at any given time. This can significantly increase the overall effectiveness of your ad-campaigns, fraud-detection systems, preventive-maintenance systems, or other critical applications underpinning your business.
In this session we will explore various modes of ingest including pipelining, pub-sub, and micro-batching, and identify the use-cases where these can be applied. We will present this in the context of open source frameworks such as Apache Flume, Kafka, among others that can be used to build related solutions. We will also present when and how to use multiple modes and frameworks together to form hybrid solutions that can address non-trivial ingest requirements with little or no extra overhead. Through this discussion we will drill-down into details of configuration and sizing for these frameworks to ensure optimal operations and utilization for long-running deployments.
This document summarizes a presentation about streaming data processing with Apache Flink. It discusses how Flink enables real-time analysis and continuous applications. Case studies are presented showing how companies like Bouygues Telecom, Zalando, King.com, and Netflix use Flink for applications like monitoring, analytics, and building a stream processing service. Flink performance is discussed through benchmarks, and features like consistent snapshots and dynamic scaling are mentioned.
This presentation will investigate how using micro-batching for submitting writes to Cassandra can improve throughput and reduce client application CPU load.
Micro-batching combines writes for the same partition key into a single network request and ensures they hit the "fast path" for writes on a Cassandra node.
About the Speaker
Adam Zegelin Technical Co-founder, Instaclustr
As Instaclustrs founding software engineer, Adam provides the foundation knowledge of our capability and engineering environment. He delivers business-focused value to our code-base and overall capability architecture. Adam is also focused on providing Instaclustr's contribution to the broader open source community on which our products and services rely, including Apache Cassandra, Apache Spark and other technologies such as CoreOS and Docker.
How to build leakproof stream processing pipelines with Apache Kafka and Apac...Cloudera, Inc.
This document discusses building leakproof stream processing pipelines with Apache Kafka and Apache Spark. It provides an overview of offset management in Spark Streaming from Kafka, including storing offsets in external data stores like ZooKeeper, Kafka, and HBase. The document also covers Spark Streaming Kafka consumer types and workflows, and addressing issues like maintaining offsets during planned and unplanned maintenance or application errors.
This talk covers the Vault 8 team's journey at Capital One where we investigated a wide variety of stream processing solutions to build a next generation real-time decisioning platform to power Capital One's infrastructure.
The result of our analysis showed Apache Storm, Apache Flink, and Apache Apex as prime contenders for our use case with Apache Apex ultimately proving to be the solution of choice based on its present readiness for enterprise deployment and its excellent performance.
How to Boost 100x Performance for Real World Application with Apache Spark-(G...Spark Summit
This document summarizes work done by an Intel software team in China to improve Apache Spark performance for real-world applications. It describes benchmarking tools like HiBench and profiling tools like HiMeter that were developed. It also discusses several case studies where the team worked with customers to optimize joins, manage memory usage, and reduce network bandwidth. The overall goal was to help solve common issues around ease of use, reliability, and scalability for Spark in production environments.
Develop Scalable Applications with DataStax Drivers (Alex Popescu, Bulat Shak...DataStax
DataStax provides modern, feature-rich, and highly tunable client libraries for C/C++, C#, Java, Node.js, Python, PHP, and Ruby that work with any cluster size no matter if deployed across multiple on premise or cloud datacenters.
Come learn right from the source about the DataStax drivers for Apache Cassandra and DSE and how they can help you build continuously available, fault tolerant, and instantly responsive applications.
About the Speakers
Alex Popescu Senior Product Manager, DataStax
I'm a developer turned product manager building developer tools for Apache Cassandra and DSE. With an eye for simplicity, I focus on creating friendly developer solutions that enable building high-performance, scalable, and fault tolerant applications. I'm passionate about open source and over years I made numerous contributions to major projects like TestNG and Groovy.
Bulat Shakirzyanov Architect, DataStax
Bulat Shakirzyanov, a.k.a. avalance123, is a software alchemist who holds a black belt in test-fu. Open source enthusiast, author of and contributor to several popular open source projects, he also loves talking about clean code, open source, unix, distributed systems, consensus algorithms and himself in third person.
This document discusses YARN federation, which allows multiple YARN clusters to be connected together. It summarizes:
- YARN is used at Microsoft for resource management but faces challenges of large scale and diverse workloads. Federation aims to address this.
- The federation architecture connects multiple independent YARN clusters through centralized services for routing, policies, and state. Applications are unaware and can seamlessly run across clusters.
- Federation policies determine how work is routed and scheduled across clusters, balancing objectives like load balancing, scaling, fairness, and isolation. A spectrum of policy options is discussed from full partitioning to full replication to dynamic partial replication.
- A demo is presented showing a job running across
Tsinghua University: Two Exemplary Applications in ChinaDataStax Academy
In this talk, we will share the experiences of applying Cassandra with two real customers in China. In the first use case, we deployed Cassandra at Sany Group, a leading company of Machinery manufacturing, to manage the sensor data generated by construction machinery. By designing a specific schema and optimizing the write process, we successfully managed over 1.5 billion historical data records and achieved the online write throughput of 10k write operations per second with 5 servers. MapReduce is also used on Cassandra for valued-added services, e.g. operations management, machine failure prediction, and abnormal behavior mining. In the second use case, Cassandra is deployed in the China Meteorological Administration to manage the Meteorological data. We design a hybrid schema to support both slice query and time window based query efficiently. Also, we explored the optimized compaction and deletion strategy for meteorological data in this case.
Processing data from social media streams and sensors in real-time is becoming increasingly prevalent and there are plenty open source solutions to choose from. To help practitioners decide what to use when we compare three popular Apache projects allowing to do stream processing: Apache Storm, Apache Spark and Apache Samza.
Apache Spark 2.4 Bridges the Gap Between Big Data and Deep LearningDataWorks Summit
Big data and AI are joined at the hip: AI applications require massive amounts of training data to build state-of-the-art models. The problem is, big data frameworks like Apache Spark and distributed deep learning frameworks like TensorFlow don’t play well together due to the disparity between how big data jobs are executed and how deep learning jobs are executed.
Apache Spark 2.4 introduced a new scheduling primitive: barrier scheduling. User can indicate Spark whether it should be using the MapReduce mode or barrier mode at each stage of the pipeline, thus it’s easy to embed distributed deep learning training as a Spark stage to simplify the training workflow. In this talk, I will demonstrate how to build a real case pipeline which combines data processing with Spark and deep learning training with TensorFlow step by step. I will also share the best practices and hands-on experiences to show the power of this new features, and bring more discussion on this topic.
The document discusses Rocana Search, a system built by Rocana to enable large scale real-time collection, processing, and analysis of event data. It aims to provide higher indexing throughput and better horizontal scaling than general purpose search systems like Solr. Key features include fully parallelized ingest and query, dynamic partitioning of data, and assigning partitions to nodes to maximize parallelism and locality. Initial benchmarks show Rocana Search can index over 3 times as many events per second as Solr.
Spark Streaming + Kafka 0.10: an integration story by Joan Viladrosa Riera at...Big Data Spain
This document provides an overview of Apache Kafka and Spark Streaming and their integration. It discusses:
- What Apache Kafka is and how it works as a publish-subscribe messaging system with topics, partitions, producers, and consumers.
- What Apache Spark Streaming is and how it provides streaming data processing using micro-batching and leveraging Spark's APIs and engine.
- The evolution of the integration between Kafka and Spark Streaming, from using receivers to the direct approach without receivers in Spark 1.3+.
- Details on how to use the new direct Kafka integration in Spark 2.0+ including location strategies, consumer strategies, and committing offsets directly to Kafka.
- Considerations around at-least
"Analyzing Twitter Data with Hadoop - Live Demo", presented at Oracle Open World 2014. The repository for the slides is in https://github.com/cloudera/cdh-twitter-example
Este documento describe las redes de computadoras y sus componentes básicos. Explica que una red es un conjunto de computadoras unidas por cables u ondas electromagnéticas. Luego detalla que inicialmente se usó el cable coaxial BNC para unir las computadoras, aunque luego fue reemplazado por cable de par trenzado con mejor aislamiento y mayor velocidad. También menciona que un ejemplo básico de red es la red telefónica y que los componentes físicos incluyen las computadoras, cables de red y software para interpretar la información
Este documento describe tres actitudes deseadas y no deseadas. La indiferencia con personas que necesitan ayuda es una actitud no deseada porque nadie querría estar en una situación donde nadie le ayuda. El irrespeto afecta negativamente la convivencia social ya que va en contra de las buenas costumbres. Ayudar al prójimo debería ser una labor diaria porque al ponerse en el lugar del otro nos motivamos a ayudar desinteresadamente.
This document outlines a 5-phase plan to flatten a classroom by increasing connectivity between students and others globally using technology. Phase 1 involves intra-class collaboration using tools like blogs, Skype, and wikis. Phase 2 adds inter-class projects using asynchronous tools like blogs, wikis, and Google Reader. Phase 3 connects classes to experts using Skype and wikis with teacher direction. Phase 4 facilitates many-to-many collaboration on writing through involvement of global experts. Phase 5 gives students self-management over teams and projects. The overall goal is to provide a global learning experience through technological connectivity.
This report analyzes whether an arrangement between Rosy and James for Rosy to pay James $40,000 annually for accounting work is a "tax avoidance arrangement" under New Zealand tax law. The report examines two tests: 1) whether the arrangement has a tax avoidance purpose or effect, and 2) whether the arrangement is consistent with Parliament's purpose based on a commercially and economically realistic interpretation. While the $40,000 fee could be viewed as remuneration for services, factors like their close family relationship and the unreasonably high fee suggest the arrangement was primarily intended to reduce Rosy's tax liability, in violation of general anti-avoidance provisions.
- There are threats of "dirty bombs", nuclear waste, and explosives in Southeast Asia near Australia that authorities have ignored, despite past plots being discovered.
- Major Australian locations that see mass gatherings like airports and parliament houses have insufficient security, despite being targets, as seen by a past discovered bomb.
- Homegrown Australian terrorists have access to materials needed for mass casualty attacks on vulnerable targets in Australia, yet efforts to address this problem have been limited and ineffective.
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This document discusses using a scientific approach and the HADI cycle to test hypotheses and gain insights. The HADI cycle involves formulating a hypothesis, taking action such as an experiment, collecting data, and gaining insights to inform the next hypothesis. This process eliminates uncertainty, allows people to work smarter rather than harder, and is at the heart of the lean startup methodology for developing minimum viable products and validating learning. Repeating the HADI cycle helps progress by replacing conventional wisdom with logical, evidence-based thinking.
El documento describe los componentes electrónicos condensadores y bobinas. Explica que los condensadores almacenan energía eléctrica en forma de campo eléctrico entre dos placas, mientras que las bobinas almacenan energía en forma de campo magnético debido a las espiras de alambre. También detalla sus construcciones y aplicaciones comunes como en sistemas de energía solar, memorias de computadoras y sistemas de ignición de autos.
Metal and Engineering update July 2016 Ian Delport
The document summarizes the poor economic conditions facing South Africa's metal and engineering industry, including high unemployment, weak demand, rising imports, and declining contribution to the economy over time. It also describes intensifying union rivalry that has led to more strikes and violence, with some unions threatening further disruptive action. Political assassinations are increasing as the rivalry between the ANC and unions like NUMSA and AMCU intensifies. The industry bargaining council faces crises as major unions and employer associations disagree. This complex environment makes accurate predictions about the future of the industry difficult.
Red hat storage objects, containers and Beyond!andreas kuncoro
Devid Casandra from Red Hat presented on different types of storage including file, object, and block storage. Red Hat offers Ceph and Gluster open source software defined storage solutions that provide scalable, self-managing storage for private clouds, containers, and other use cases. Recent improvements include multi-site replication, a new BlueStore backend for Ceph, and container-based deployment options. A unified management solution is also in development.
Query Expansion with Locally-Trained Word Embeddings (Neu-IR 2016)Bhaskar Mitra
This document discusses using locally-trained word embeddings for query expansion. It shows that training word embeddings on documents relevant to a query (local model) provides a better representation than training globally on the entire corpus. In experiments on three datasets, the local model improved average NDCG@10 scores over using global embeddings or no expansion. The local model identifies query and expansion terms more closely related to relevant documents. Future work could improve effectiveness and efficiency of the local model approach.
Apache cassandra & apache spark for time series dataPatrick McFadin
Apache Cassandra is a distributed database that stores time series data in a partitioned and ordered format. Apache Spark can efficiently query this Cassandra data using Resilient Distributed Datasets (RDDs) and perform analytics like aggregations. For example, weather station data stored sequentially in Cassandra by time can be aggregated into daily high and low temperatures with Spark and written back to a roll-up Cassandra table.
Kudu: Resolving Transactional and Analytic Trade-offs in Hadoopjdcryans
Kudu is a new column-oriented storage system for Apache Hadoop that is designed to address the gaps in transactional processing and analytics in Hadoop. It aims to provide high throughput for large scans, low latency for individual rows, and database semantics like ACID transactions. Kudu is motivated by the changing hardware landscape with faster SSDs and more memory, and aims to take advantage of these advances. It uses a distributed table design partitioned into tablets replicated across servers, with a centralized metadata service for coordination.
DatEngConf SF16 - Apache Kudu: Fast Analytics on Fast DataHakka Labs
This document discusses Apache Kudu, an open source columnar storage system for analytics workloads on Hadoop. Kudu is designed to enable both fast analytics queries as well as real-time updates on fast changing data. It aims to fill gaps in the current Hadoop storage landscape by supporting simultaneous high throughput scans, low latency reads/writes, and ACID transactions. An example use case described is for real-time fraud detection on streaming financial data.
Kudu: New Hadoop Storage for Fast Analytics on Fast DataCloudera, Inc.
The document discusses Kudu, an open source storage system for Hadoop that is designed to enable both transactional and analytic workloads. Kudu uses a columnar storage format and provides ACID transactions for fast analytics on fast data. It aims to address gaps in Hadoop for workloads that require simultaneous random access and scanning of data. Benchmarks show Kudu can perform TPC-H queries within 2x of Parquet storage, with low latency for reads and writes on solid state drives.
February 2016 HUG: Apache Kudu (incubating): New Apache Hadoop Storage for Fa...Yahoo Developer Network
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Apache Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets. Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads. This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark, that fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.
Speakers:
David Alves. Software engineer at Cloudera working on the Kudu team, and a PhD student at UT Austin. David is a committer at the Apache Software Foundation and has contributed to several open source projects, including Apache Cassandra and Apache Drill.
Kudu is a storage layer developed by Cloudera that is designed for fast analytics on fast data. It aims to provide high throughput for large scans and low latency for individual reads and writes. Kudu tables can be queried using SQL and provide database-like semantics like ACID transactions. Kudu is optimized for workloads that require both sequential and random read/write access patterns, such as time series data, machine data analytics, and online reporting. It provides improvements over traditional Hadoop storage systems by eliminating complex ETL pipelines and enabling immediate access to new data.
This document discusses Kudu, an open source storage system for Hadoop that provides fast analytics on fast data. It was built by Cloudera to address gaps in Hadoop's storage technologies by providing low-latency transactions and fast scans. The document outlines Kudu's design goals, architecture using columnar storage and Raft consensus, performance benchmarks showing faster analytics than Parquet and HBase, and two use cases at Chinese company Xiaomi where Kudu improved their analytics pipelines.
Apache Kudu (Incubating): New Hadoop Storage for Fast Analytics on Fast Data ...Cloudera, Inc.
This document provides an overview of Apache Kudu, an open source storage layer for Apache Hadoop that enables fast analytics on fast data. Some key points:
- Kudu is a columnar storage engine that allows for both fast analytics queries as well as low-latency updates to the stored data.
- It addresses gaps in the existing Hadoop storage landscape by providing efficient scans, individual row lookups, and mutable data all within the same system.
- Kudu uses a master-tablet server architecture with tablets that are horizontally partitioned and replicated for fault tolerance. It supports SQL and NoSQL interfaces.
- Integrations with Spark, Impala and MapReduce allow it to be used for both
Kudu is a storage engine for Hadoop designed to address gaps in Hadoop's ability to handle workloads that require both high-throughput data ingestion and low-latency random access. It is a columnar storage engine that uses a log-structured merge tree to store data and provides APIs for NoSQL and SQL access. Kudu aims to provide high performance for both scans and random access through its columnar design and tablet architecture that partitions data across servers.
Using Kafka and Kudu for fast, low-latency SQL analytics on streaming dataMike Percy
The document discusses using Kafka and Kudu for low-latency SQL analytics on streaming data. It describes the challenges of supporting both streaming and batch workloads simultaneously using traditional solutions. The authors propose using Kafka to ingest data and Kudu for structured storage and querying. They demonstrate how this allows for stream processing, batch processing, and querying of up-to-second data with low complexity. Case studies from Xiaomi and TPC-H benchmarks show the advantages of this approach over alternatives.
Introduction to Kudu - StampedeCon 2016StampedeCon
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem that fills the gap described above, complementing HDFS and HBase to provide a new option to achieve fast scans and fast random access from a single API.
3 Things to Learn About:
-How Kudu is able to fill the analytic gap between HDFS and Apache HBase
-The trade-offs between real-time transactional access and fast analytic performance
-How Kudu provides an option to achieve fast scans and random access from a single API
Introducing Apache Kudu (Incubating) - Montreal HUG May 2016Mladen Kovacevic
The document introduces Apache Kudu (incubating), a new updatable columnar storage system for Apache Hadoop designed for fast analytics on fast and changing data. It was designed to simplify architectures that use HDFS and HBase together. Kudu aims to provide high throughput for scans, low latency for individual rows, and database-like ACID transactions. It uses a columnar format and is optimized for SSD and new storage technologies.
Introducing Kudu, Big Data Warehousing MeetupCaserta
Not just an SQL interface or file system, Kudu - the new, updating column store for Hadoop, is changing the storage landscape. It's easy to operate and makes new data immediately available for analytics or operations.
At the Caserta Concepts Big Data Warehousing Meetup, our guests from Cloudera outlined the functionality of Kudu and talked about why it will become an integral component in big data warehousing on Hadoop.
To learn more about what Caserta Concepts has to offer, visit http://casertaconcepts.com/
Jeremy Beard, a senior solutions architect at Cloudera, introduces Kudu, a new column-oriented storage system for Apache Hadoop designed for fast analytics on fast changing data. Kudu is meant to fill gaps in HDFS and HBase by providing efficient scanning, finding and writing capabilities simultaneously. It uses a relational data model with ACID transactions and integrates with common Hadoop tools like Impala, Spark and MapReduce. Kudu aims to simplify real-time analytics use cases by allowing data to be directly updated without complex ETL processes.
The document discusses Apache Kudu, an open source storage layer for Apache Hadoop that enables fast analytics on fast data. Kudu is designed to fill the gap between HDFS and HBase by providing fast analytics capabilities on fast-changing or frequently updated data. It achieves this through its scalable and fast tabular storage design that allows for both high insert/update throughput and fast scans/queries. The document provides an overview of Kudu's architecture and capabilities, examples of how to use its NoSQL and SQL APIs, and real-world use cases like enabling low-latency analytics pipelines for companies like Xiaomi.
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
The document is a presentation about using Hadoop for analytic workloads. It discusses how Hadoop has traditionally been used for batch processing but can now also be used for interactive queries and business intelligence workloads using tools like Impala, Parquet, and HDFS. It summarizes performance tests showing Impala can outperform MapReduce for queries and scales linearly with additional nodes. The presentation argues Hadoop provides an effective solution for certain data warehouse workloads while maintaining flexibility, ease of scaling, and cost effectiveness.
Eight tips are provided for deploying DevSecOps:
1. Embrace automation and prepare security teams for automated integration with DevOps initiatives.
2. Enable security testing tools and processes earlier in the development process.
3. Prioritize automated tools that can quickly triage critical issues to reduce false positives.
4. Start identifying open source components and vulnerabilities in development as a high priority.
Building a system for machine and event-oriented data - SF HUG Nov 2015Felicia Haggarty
- The document discusses Rocana's system for processing machine and event data in real-time at high volumes.
- The system ingests all types of event data, models everything as events, and uses Apache Kafka and consumers to process and analyze the events.
- The system provides guarantees around no single points of failure, horizontal scalability, and exactly-once delivery while events are transformed and aggregated into metrics and stored.
Impala is an open-source SQL query engine for Hadoop that is designed for performance. It utilizes standard Hadoop components like HDFS, HBase, and YARN. Impala allows users to issue SQL queries against data stored in HDFS and HBase and returns results very quickly. It exposes industry-standard interfaces that allow business intelligence tools to connect. Impala has added many new features in recent versions like analytic functions, subqueries, and support for joining and aggregating data that can spill to disk.
This document discusses the data revolution and how open source software like Hadoop led to new ways of working with large amounts of data by providing an enterprise data hub that functions like a multi-tool for data analysis and management. Hadoop caught on and an entire ecosystem grew around it, with enterprises adopting this approach and building systems like data hubs on a foundation of open source software and a model of data sharing built on trust.
Whither the Hadoop Developer Experience, June Hadoop Meetup, Nitin MotgiFelicia Haggarty
The document discusses challenges with building operational data applications on Hadoop and introduces the Cask Data Application Platform (CDAP) as a solution. It provides an agenda that covers data applications, challenges, CDAP motivation and goals, use cases, and an introduction and architecture overview of CDAP. The document aims to demonstrate how CDAP provides a unified platform that simplifies application development and lifecycle while supporting reusable data and processing patterns.
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
The Challenge of Interpretability in Generative AI Models.pdfSara Kroft
Navigating the intricacies of generative AI models reveals a pressing challenge: interpretability. Our blog delves into the complexities of understanding how these advanced models make decisions, shedding light on the mechanisms behind their outputs. Explore the latest research, practical implications, and ethical considerations, as we unravel the opaque processes that drive generative AI. Join us in this insightful journey to demystify the black box of artificial intelligence.
Dive into the complexities of generative AI with our blog on interpretability. Find out why making AI models understandable is key to trust and ethical use and discover current efforts to tackle this big challenge.
Self-Healing Test Automation Framework - HealeniumKnoldus Inc.
Revolutionize your test automation with Healenium's self-healing framework. Automate test maintenance, reduce flakes, and increase efficiency. Learn how to build a robust test automation foundation. Discover the power of self-healing tests. Transform your testing experience.
Redefining Cybersecurity with AI CapabilitiesPriyanka Aash
In this comprehensive overview of Cisco's latest innovations in cybersecurity, the focus is squarely on resilience and adaptation in the face of evolving threats. The discussion covers the imperative of tackling Mal information, the increasing sophistication of insider attacks, and the expanding attack surfaces in a hybrid work environment. Emphasizing a shift towards integrated platforms over fragmented tools, Cisco introduces its Security Cloud, designed to provide end-to-end visibility and robust protection across user interactions, cloud environments, and breaches. AI emerges as a pivotal tool, from enhancing user experiences to predicting and defending against cyber threats. The blog underscores Cisco's commitment to simplifying security stacks while ensuring efficacy and economic feasibility, making a compelling case for their platform approach in safeguarding digital landscapes.
How UiPath Discovery Suite supports identification of Agentic Process Automat...DianaGray10
📚 Understand the basics of the newly persona-based LLM-powered Agentic Process Automation and discover how existing UiPath Discovery Suite products like Communication Mining, Process Mining, and Task Mining can be leveraged to identify APA candidates.
Topics Covered:
💡 Idea Behind APA: Explore the innovative concept of Agentic Process Automation and its significance in modern workflows.
🔄 How APA is Different from RPA: Learn the key differences between Agentic Process Automation and Robotic Process Automation.
🚀 Discover the Advantages of APA: Uncover the unique benefits of implementing APA in your organization.
🔍 Identifying APA Candidates with UiPath Discovery Products: See how UiPath's Communication Mining, Process Mining, and Task Mining tools can help pinpoint potential APA candidates.
🔮 Discussion on Expected Future Impacts: Engage in a discussion on the potential future impacts of APA on various industries and business processes.
Enhance your knowledge on the forefront of automation technology and stay ahead with Agentic Process Automation. 🧠💼✨
Speakers:
Arun Kumar Asokan, Delivery Director (US) @ qBotica and UiPath MVP
Naveen Chatlapalli, Solution Architect @ Ashling Partners and UiPath MVP
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.
Demystifying Neural Networks And Building Cybersecurity ApplicationsPriyanka Aash
In today's rapidly evolving technological landscape, Artificial Neural Networks (ANNs) have emerged as a cornerstone of artificial intelligence, revolutionizing various fields including cybersecurity. Inspired by the intricacies of the human brain, ANNs have a rich history and a complex structure that enables them to learn and make decisions. This blog aims to unravel the mysteries of neural networks, explore their mathematical foundations, and demonstrate their practical applications, particularly in building robust malware detection systems using Convolutional Neural Networks (CNNs).
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.
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.
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.