This document provides an overview of SolrCloud on Hadoop. It discusses how SolrCloud allows for distributed, highly scalable search capabilities on Hadoop clusters. Key components that work with SolrCloud are also summarized, including HDFS for storage, MapReduce for processing, and ZooKeeper for coordination services. The document demonstrates how SolrCloud can index and query large datasets stored in Hadoop.
This document provides an overview of operating HBase clusters in production environments. It discusses leveraging existing knowledge of distributed systems, getting metrics set up using tools like Ganglia and OpenTSDB, automating tasks with Puppet, Chef and Fabric, setting up alerting with Nagios and Zabbix, and different backup strategies for HBase including offline distcp backups, replication to another cluster, and using HBase snapshots. The goals are to help operations teams understand how to manage HBase and empower them to work with their own operations organizations.
Speed Up Your Existing Relational Databases with Hazelcast and SpeedmentHazelcast
In this webinar
How do you get data from your existing relational databases changed by third party applications into your Hazelcast maps? How do you accomplish this if you have several databases, located on different sites, that need to be aggregated into a global Hazelcast map? How is it possible to reflect data from a relational database that has ten thousand updates per second or more?
Speedment’s SQL Reflector makes it possible to integrate your existing relational data with continuous updates of Hazelcast data-maps in real-time. In this webinar, we will show a couple of real-world cases where database applications are speeded up using Hazelcast maps fed by Speedment. We will also demonstrate how easily your existing database can be “reverse engineered” by the Speedment software that automatically creates efficient Java POJOs that can be used directly by Hazelcast.
We’ll cover these topics:
-Joint solution case studies
-Demo
-Live Q&A
Presenter:
Per-Åke Minborg, CTO at Speedment
Per-Åke Minborg is founder and CTO at Speedment AB. He is a passionate Java developer, dedicated to OpenSource software and an expert in finding new ways of solving problems – the harder problem the better. As a result, he has 15+ US patent applications and invention disclosures. He has a deep understanding of in-memory databases, high-performance solutions, cloud technologies and concurrent programming. He has previously served as CTO and founder of Chilirec and the Phone Pages. Per-Åke has a M.Sc. in Electrical Engineering from Chalmers University of Technology and several years of studies in computer science and computer security at university and PhD level.
Hortonworks provides best practices for system testing Hadoop clusters. It recommends testing across different operating systems, configurations, workloads and hardware to mimic a production environment. The document outlines automating the testing process through continuous integration to test over 15,000 configurations. It provides guidance on test planning, including identifying requirements, selecting hardware and workloads to test upgrades, migrations and changes to security settings.
Rigorous and Multi-tenant HBase Performance MeasurementDataWorks Summit
The document discusses techniques for rigorously measuring HBase performance in both standalone and multi-tenant environments. It begins with an overview of HBase and the Yahoo! Cloud Serving Benchmark (YCSB) for evaluating databases. It then discusses best practices for cluster setup, data loading, and benchmarking techniques like warming the cache, setting target throughput, and using appropriate workloads. Finally, it covers challenges in measuring HBase performance when used alongside other frameworks like MapReduce and Solr in a multi-tenant setting.
Elastic HBase on Mesos aims to improve resource utilization of HBase clusters by running HBase in Docker containers managed by Mesos and Marathon. This allows HBase clusters to dynamically scale based on varying workload demands, increases utilization by running mixed workloads on shared resources, and simplifies operations through standard containerization. Key benefits include easier management, higher efficiency through elastic scaling and resource sharing, and improved cluster tunability.
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.
This document provides an overview of SolrCloud on Hadoop. It discusses how SolrCloud allows for distributed, highly scalable search capabilities on Hadoop clusters. Key components that work with SolrCloud are also summarized, including HDFS for storage, MapReduce for processing, and ZooKeeper for coordination services. The document demonstrates how SolrCloud can index and query large datasets stored in Hadoop.
This document provides an overview of operating HBase clusters in production environments. It discusses leveraging existing knowledge of distributed systems, getting metrics set up using tools like Ganglia and OpenTSDB, automating tasks with Puppet, Chef and Fabric, setting up alerting with Nagios and Zabbix, and different backup strategies for HBase including offline distcp backups, replication to another cluster, and using HBase snapshots. The goals are to help operations teams understand how to manage HBase and empower them to work with their own operations organizations.
Speed Up Your Existing Relational Databases with Hazelcast and SpeedmentHazelcast
In this webinar
How do you get data from your existing relational databases changed by third party applications into your Hazelcast maps? How do you accomplish this if you have several databases, located on different sites, that need to be aggregated into a global Hazelcast map? How is it possible to reflect data from a relational database that has ten thousand updates per second or more?
Speedment’s SQL Reflector makes it possible to integrate your existing relational data with continuous updates of Hazelcast data-maps in real-time. In this webinar, we will show a couple of real-world cases where database applications are speeded up using Hazelcast maps fed by Speedment. We will also demonstrate how easily your existing database can be “reverse engineered” by the Speedment software that automatically creates efficient Java POJOs that can be used directly by Hazelcast.
We’ll cover these topics:
-Joint solution case studies
-Demo
-Live Q&A
Presenter:
Per-Åke Minborg, CTO at Speedment
Per-Åke Minborg is founder and CTO at Speedment AB. He is a passionate Java developer, dedicated to OpenSource software and an expert in finding new ways of solving problems – the harder problem the better. As a result, he has 15+ US patent applications and invention disclosures. He has a deep understanding of in-memory databases, high-performance solutions, cloud technologies and concurrent programming. He has previously served as CTO and founder of Chilirec and the Phone Pages. Per-Åke has a M.Sc. in Electrical Engineering from Chalmers University of Technology and several years of studies in computer science and computer security at university and PhD level.
Hortonworks provides best practices for system testing Hadoop clusters. It recommends testing across different operating systems, configurations, workloads and hardware to mimic a production environment. The document outlines automating the testing process through continuous integration to test over 15,000 configurations. It provides guidance on test planning, including identifying requirements, selecting hardware and workloads to test upgrades, migrations and changes to security settings.
Rigorous and Multi-tenant HBase Performance MeasurementDataWorks Summit
The document discusses techniques for rigorously measuring HBase performance in both standalone and multi-tenant environments. It begins with an overview of HBase and the Yahoo! Cloud Serving Benchmark (YCSB) for evaluating databases. It then discusses best practices for cluster setup, data loading, and benchmarking techniques like warming the cache, setting target throughput, and using appropriate workloads. Finally, it covers challenges in measuring HBase performance when used alongside other frameworks like MapReduce and Solr in a multi-tenant setting.
Elastic HBase on Mesos aims to improve resource utilization of HBase clusters by running HBase in Docker containers managed by Mesos and Marathon. This allows HBase clusters to dynamically scale based on varying workload demands, increases utilization by running mixed workloads on shared resources, and simplifies operations through standard containerization. Key benefits include easier management, higher efficiency through elastic scaling and resource sharing, and improved cluster tunability.
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.
HBaseCon 2012 | HBase, the Use Case in eBay Cassini Cloudera, Inc.
eBay marketplace has been working hard on the next generation search infrastructure and software system, code-named Cassini. The new search engine processes over 250 million search queries and serves more than 2 billion page views each day. Its indexing platform is based on Apache Hadoop and Apache HBase. Apache HBase is a distributed persistent layer built on Hadoop to support billions of updates per day. Its easy sharding character, fast writes, and table scans, super fast data bulk load, and natural integration to Hadoop provide the cornerstones for successful continuous index builds. We will share with the audience the technical details and share the difficulties and challenges that we’ve gone through and that we are still facing in the process.
Are you taking advantage of all of Hadoop’s features to operate a stable and effective cluster? Inspired by real-world support cases, this talk discusses best practices and new features to help improve incident response and daily operations. Chances are that you’ll walk away from this talk with some new ideas to implement in your own clusters.
This document summarizes Johan Gustavsson's presentation on scaling Hadoop in the cloud. It discusses replacing an on-premise Hadoop cluster with Plazma storage on S3 and job execution in isolated pools. It also covers Treasure Data's Patchset project which aims to support multiple Hadoop versions and allow job-preserving restarts of the Elephant server.
Johnny Miller – Cassandra + Spark = Awesome- NoSQL matters Barcelona 2014NoSQLmatters
Johnny Miller – Cassandra + Spark = Awesome
This talk will discuss how Cassandra and Spark can work together to deliver real-time analytics. This is a technical discussion that will introduce the attendees to the basic principals on Cassandra and Spark, why they work well together and examples usecases.
January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transacti...Yahoo Developer Network
Monte Zweben Co-Founder and CEO of Splice Machine, will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing.
Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update.
In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle.
HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing.
The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.
Flexible and Real-Time Stream Processing with Apache FlinkDataWorks Summit
This document provides an overview of stream processing with Apache Flink. It discusses the rise of stream processing and how it enables low-latency applications and real-time analysis. It then describes Flink's stream processing capabilities, including pipelining of data, fault tolerance through checkpointing and recovery, and integration with batch processing. The document also summarizes Flink's programming model, state management, and roadmap for further development.
Adobe has packaged HBase in Docker containers and uses Marathon and Mesos to schedule them—allowing us to decouple the RegionServer from the host, express resource requirements declaratively, and open the door for unassisted real-time deployments, elastic (up and down) real-time scalability, and more. In this talk, you'll hear what we've learned and explain why this approach could fundamentally change HBase operations.
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketCloudera, Inc.
Solbase is an exciting new open-source, real-time search engine being developed at Photobucket to service the over 30 million daily search requests Photobucket handles. Solbase replaces Lucene’s file system-based index with HBase. This allows the system to update in real-time and linearly scale to serve millions of daily search requests on a large dataset. This session will explore the architecture of Solbase as well as some of Lucene/Solr’s inherent issues we overcame. Finally, we’ll go over performance metrics of Solbase against production traffic.
This document provides an overview and lessons learned from Hadoop. It discusses why Hadoop is used, how MapReduce and HDFS work, tips for integration and operations, and the outlook for the Hadoop community moving forward with real-time capabilities and refined APIs. Key takeaways include only using Hadoop if necessary, fully understanding your data pipeline, and "unboxing the black box" of Hadoop.
HBaseCon 2013: Apache HBase Operations at PinterestCloudera, Inc.
This document summarizes Jeremy Carroll's presentation on HBase operations at Amazon. It discusses how Amazon uses HBase across 5 clusters with billions of page views. Key points include:
- HBase is deployed on Amazon Web Services using CDH and customized for EC2 instability and lack of rack locality
- Puppet is used to provision nodes and apply custom HDFS and HBase configurations
- Extensive monitoring of the clusters is done using OpenTSDB, Ganglia, and custom dashboards to ensure high availability
- Various techniques are used to optimize performance, handle large volumes, and back up data on EC2 infrastructure.
Tez Shuffle Handler: Shuffling at Scale with Apache HadoopDataWorks Summit
In this talk we introduce a new Shuffle Handler for Tez, a YARN Auxiliary Service, that addresses the shortcomings and performance bottlenecks of the legacy MapReduce Shuffle Handler, the default shuffle service in Apache Tez. Based on our experiences of running Apache Pig and *Hive at scale on Apache Tez at Yahoo!, advanced features like auto-parallelism and session mode expose specific limitations in the shuffle service which was not designed with these features in mind.
A highly auto-reduced job suffers from longer fetch times as the number of fetches per downstream task increases by the auto-reduction factor. The Apache Tez Shuffle Handler adds composite fetch which has support for multi-partition fetch to mitigate this performance slow down.
Also, since Apache Tez DAGs are run completely within a single application unlike their equivalent MapReduce jobs, intermediate shuffle data in Tez can linger beyond its usefulness. The Apache Tez Shuffle Handler provides deletion APIs to reduce disk usage for such long running Tez sessions.
As an emerging technology we will outline future roadmap for the Apache Tez Shuffle Handler and provide performance evaluation results from real world jobs at scale.
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.
NYC HUG - Application Architectures with Apache Hadoopmarkgrover
This document summarizes Mark Grover's presentation on application architectures with Apache Hadoop. It discusses processing clickstream data from web logs using techniques like deduplication, filtering, and sessionization in Hadoop. Specifically, it describes how to implement sessionization in MapReduce by using the user's IP address and timestamp to group log lines into sessions in the reducer.
Boosting Machine Learning with Redis Modules and SparkDvir Volk
Redis modules allow for new capabilities like machine learning models to be added to Redis. The Redis-ML module stores machine learning models like random forests and supports operations like model training, evaluation, and prediction directly from Redis for low latency. Spark can be used to train models which are then saved as Redis modules, allowing models to be easily deployed and accessed from services and clients.
Managing multi tenant resource toward Hive 2.0Kai Sasaki
This document discusses Treasure Data's migration architecture for managing resources across multiple clusters when upgrading from Hive 1.x to Hive 2.0. It introduces components like PerfectQueue and Plazma that enable blue-green deployment without downtime. It also describes how automatic testing and validation is done to prevent performance degradation. Resource management is discussed to define resources per account across different job queues and Hadoop clusters. Brief performance comparisons show improvements from Hive 2.x features like Tez and vectorization.
This document discusses building applications on Hadoop and introduces the Kite SDK. It provides an overview of Hadoop and its components like HDFS and MapReduce. It then discusses that while Hadoop is powerful and flexible, it can be complex and low-level, making application development challenging. The Kite SDK aims to address this by providing higher-level APIs and abstractions to simplify common use cases and allow developers to focus on business logic rather than infrastructure details. It includes modules for data, ETL processing with Morphlines, and tools for working with datasets and jobs. The SDK is open source and supports modular adoption.
Gruter TECHDAY 2014 Realtime Processing in TelcoGruter
Big Telco, Bigger real-time demands: Real-time processing in Telco
- Presented by Jung-ryong Lee, engineer manager at SK Telecom at Gruter TECHDAY 2014 Oct.29 Seoul, Korea
Building and Running Solr-as-a-Service: Presented by Shai Erera, IBMLucidworks
This document discusses building and running Solr as a service in the cloud. It covers:
- The challenges of deploying Solr in cloud environments and the need for a managed service.
- The architecture of the Solr-as-a-Service, which uses Docker, Mesos, and other tools to provide multi-tenant Solr clusters.
- Key aspects of managing Solr clusters in the cloud service, including software upgrades, resizing clusters, handling replicas, and balancing clusters.
- The document discusses several common "anti-patterns" encountered when working with big data, including treating small datasets as big data, relying on a single tool for all jobs, improper data integration techniques, inefficient queries, and not considering security.
- It provides recommendations to avoid these anti-patterns such as using appropriate tools for dataset size, choosing best-in-class tools for each job, integrating data with Kafka, optimizing queries, and implementing security controls.
- The key message is that a polyglot approach is needed to leverage the best tools for each use case when working with big data.
The letter begins by explaining that the writer has been very busy being taken on visits by their guardian, Mrs. Haggerty, to prepare for their debut into society. They go to shops and make acquaintance visits almost daily. However, the writer finds the social rituals tedious and would prefer quieter activities like reading. They miss their mother and finding socializing exhausting.
Vitamins are organic nutrients required by the body in small amounts for various biochemical functions. They are essential for providing good health but must be obtained through diet or supplements as the body cannot produce them. There are 13 vitamins needed for development, which help make enzymes and hormones, convert food to energy, and support growth, metabolism and general health. Deficiencies can result in various symptoms depending on the vitamin lacking in the diet.
HBaseCon 2012 | HBase, the Use Case in eBay Cassini Cloudera, Inc.
eBay marketplace has been working hard on the next generation search infrastructure and software system, code-named Cassini. The new search engine processes over 250 million search queries and serves more than 2 billion page views each day. Its indexing platform is based on Apache Hadoop and Apache HBase. Apache HBase is a distributed persistent layer built on Hadoop to support billions of updates per day. Its easy sharding character, fast writes, and table scans, super fast data bulk load, and natural integration to Hadoop provide the cornerstones for successful continuous index builds. We will share with the audience the technical details and share the difficulties and challenges that we’ve gone through and that we are still facing in the process.
Are you taking advantage of all of Hadoop’s features to operate a stable and effective cluster? Inspired by real-world support cases, this talk discusses best practices and new features to help improve incident response and daily operations. Chances are that you’ll walk away from this talk with some new ideas to implement in your own clusters.
This document summarizes Johan Gustavsson's presentation on scaling Hadoop in the cloud. It discusses replacing an on-premise Hadoop cluster with Plazma storage on S3 and job execution in isolated pools. It also covers Treasure Data's Patchset project which aims to support multiple Hadoop versions and allow job-preserving restarts of the Elephant server.
Johnny Miller – Cassandra + Spark = Awesome- NoSQL matters Barcelona 2014NoSQLmatters
Johnny Miller – Cassandra + Spark = Awesome
This talk will discuss how Cassandra and Spark can work together to deliver real-time analytics. This is a technical discussion that will introduce the attendees to the basic principals on Cassandra and Spark, why they work well together and examples usecases.
January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transacti...Yahoo Developer Network
Monte Zweben Co-Founder and CEO of Splice Machine, will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing.
Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update.
In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle.
HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing.
The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.
Flexible and Real-Time Stream Processing with Apache FlinkDataWorks Summit
This document provides an overview of stream processing with Apache Flink. It discusses the rise of stream processing and how it enables low-latency applications and real-time analysis. It then describes Flink's stream processing capabilities, including pipelining of data, fault tolerance through checkpointing and recovery, and integration with batch processing. The document also summarizes Flink's programming model, state management, and roadmap for further development.
Adobe has packaged HBase in Docker containers and uses Marathon and Mesos to schedule them—allowing us to decouple the RegionServer from the host, express resource requirements declaratively, and open the door for unassisted real-time deployments, elastic (up and down) real-time scalability, and more. In this talk, you'll hear what we've learned and explain why this approach could fundamentally change HBase operations.
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketCloudera, Inc.
Solbase is an exciting new open-source, real-time search engine being developed at Photobucket to service the over 30 million daily search requests Photobucket handles. Solbase replaces Lucene’s file system-based index with HBase. This allows the system to update in real-time and linearly scale to serve millions of daily search requests on a large dataset. This session will explore the architecture of Solbase as well as some of Lucene/Solr’s inherent issues we overcame. Finally, we’ll go over performance metrics of Solbase against production traffic.
This document provides an overview and lessons learned from Hadoop. It discusses why Hadoop is used, how MapReduce and HDFS work, tips for integration and operations, and the outlook for the Hadoop community moving forward with real-time capabilities and refined APIs. Key takeaways include only using Hadoop if necessary, fully understanding your data pipeline, and "unboxing the black box" of Hadoop.
HBaseCon 2013: Apache HBase Operations at PinterestCloudera, Inc.
This document summarizes Jeremy Carroll's presentation on HBase operations at Amazon. It discusses how Amazon uses HBase across 5 clusters with billions of page views. Key points include:
- HBase is deployed on Amazon Web Services using CDH and customized for EC2 instability and lack of rack locality
- Puppet is used to provision nodes and apply custom HDFS and HBase configurations
- Extensive monitoring of the clusters is done using OpenTSDB, Ganglia, and custom dashboards to ensure high availability
- Various techniques are used to optimize performance, handle large volumes, and back up data on EC2 infrastructure.
Tez Shuffle Handler: Shuffling at Scale with Apache HadoopDataWorks Summit
In this talk we introduce a new Shuffle Handler for Tez, a YARN Auxiliary Service, that addresses the shortcomings and performance bottlenecks of the legacy MapReduce Shuffle Handler, the default shuffle service in Apache Tez. Based on our experiences of running Apache Pig and *Hive at scale on Apache Tez at Yahoo!, advanced features like auto-parallelism and session mode expose specific limitations in the shuffle service which was not designed with these features in mind.
A highly auto-reduced job suffers from longer fetch times as the number of fetches per downstream task increases by the auto-reduction factor. The Apache Tez Shuffle Handler adds composite fetch which has support for multi-partition fetch to mitigate this performance slow down.
Also, since Apache Tez DAGs are run completely within a single application unlike their equivalent MapReduce jobs, intermediate shuffle data in Tez can linger beyond its usefulness. The Apache Tez Shuffle Handler provides deletion APIs to reduce disk usage for such long running Tez sessions.
As an emerging technology we will outline future roadmap for the Apache Tez Shuffle Handler and provide performance evaluation results from real world jobs at scale.
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.
NYC HUG - Application Architectures with Apache Hadoopmarkgrover
This document summarizes Mark Grover's presentation on application architectures with Apache Hadoop. It discusses processing clickstream data from web logs using techniques like deduplication, filtering, and sessionization in Hadoop. Specifically, it describes how to implement sessionization in MapReduce by using the user's IP address and timestamp to group log lines into sessions in the reducer.
Boosting Machine Learning with Redis Modules and SparkDvir Volk
Redis modules allow for new capabilities like machine learning models to be added to Redis. The Redis-ML module stores machine learning models like random forests and supports operations like model training, evaluation, and prediction directly from Redis for low latency. Spark can be used to train models which are then saved as Redis modules, allowing models to be easily deployed and accessed from services and clients.
Managing multi tenant resource toward Hive 2.0Kai Sasaki
This document discusses Treasure Data's migration architecture for managing resources across multiple clusters when upgrading from Hive 1.x to Hive 2.0. It introduces components like PerfectQueue and Plazma that enable blue-green deployment without downtime. It also describes how automatic testing and validation is done to prevent performance degradation. Resource management is discussed to define resources per account across different job queues and Hadoop clusters. Brief performance comparisons show improvements from Hive 2.x features like Tez and vectorization.
This document discusses building applications on Hadoop and introduces the Kite SDK. It provides an overview of Hadoop and its components like HDFS and MapReduce. It then discusses that while Hadoop is powerful and flexible, it can be complex and low-level, making application development challenging. The Kite SDK aims to address this by providing higher-level APIs and abstractions to simplify common use cases and allow developers to focus on business logic rather than infrastructure details. It includes modules for data, ETL processing with Morphlines, and tools for working with datasets and jobs. The SDK is open source and supports modular adoption.
Gruter TECHDAY 2014 Realtime Processing in TelcoGruter
Big Telco, Bigger real-time demands: Real-time processing in Telco
- Presented by Jung-ryong Lee, engineer manager at SK Telecom at Gruter TECHDAY 2014 Oct.29 Seoul, Korea
Building and Running Solr-as-a-Service: Presented by Shai Erera, IBMLucidworks
This document discusses building and running Solr as a service in the cloud. It covers:
- The challenges of deploying Solr in cloud environments and the need for a managed service.
- The architecture of the Solr-as-a-Service, which uses Docker, Mesos, and other tools to provide multi-tenant Solr clusters.
- Key aspects of managing Solr clusters in the cloud service, including software upgrades, resizing clusters, handling replicas, and balancing clusters.
- The document discusses several common "anti-patterns" encountered when working with big data, including treating small datasets as big data, relying on a single tool for all jobs, improper data integration techniques, inefficient queries, and not considering security.
- It provides recommendations to avoid these anti-patterns such as using appropriate tools for dataset size, choosing best-in-class tools for each job, integrating data with Kafka, optimizing queries, and implementing security controls.
- The key message is that a polyglot approach is needed to leverage the best tools for each use case when working with big data.
The letter begins by explaining that the writer has been very busy being taken on visits by their guardian, Mrs. Haggerty, to prepare for their debut into society. They go to shops and make acquaintance visits almost daily. However, the writer finds the social rituals tedious and would prefer quieter activities like reading. They miss their mother and finding socializing exhausting.
Vitamins are organic nutrients required by the body in small amounts for various biochemical functions. They are essential for providing good health but must be obtained through diet or supplements as the body cannot produce them. There are 13 vitamins needed for development, which help make enzymes and hormones, convert food to energy, and support growth, metabolism and general health. Deficiencies can result in various symptoms depending on the vitamin lacking in the diet.
This document provides a 3 sentence summary of a Python 2.4 quick reference card:
The document lists various Python environment variables, command line options, file extensions, language keywords, built-in functions, data types, statements, operators, modules and more. It provides information on the Python interpreter, language syntax, standard library modules, and how to execute Python scripts and use Python interactively from the command line. The quick reference card serves as a concise guide to key elements of the Python 2.4 language.
From SOA and SCA to FraSCAti discusses Service-Oriented Architecture (SOA) and Service Component Architecture (SCA). It introduces FraSCAti, an open source SCA runtime that provides a reflective component model and framework for SOA interoperability, integration, and runtime adaptability. FraSCAti extends SCA with features like aspect components, a software product line approach, and APIs for dynamic reconfiguration.
An observer is watching someone and notes that it is 08:53 and the person being watched is doing nothing, as they often do at that time. The observer expresses frustration at the watched person's lack of activity.
The document discusses spinal and spinal cord trauma. It provides details on:
1) The epidemiology of spinal injuries, including that motor vehicle collisions are the leading cause, accounting for 42% of cases. Falls account for 27% of injuries.
2) The functional anatomy of the spine, which consists of 33 vertebrae providing structure and protection for the spinal cord. Injuries can damage the bony elements or neural elements.
3) Methods for assessing spinal stability after injury, including the Denis three-column principle which states an injury is unstable if it disrupts two or more columns of the spine. Vertebral fractures over 25% in the cervical spine or 50% in the thoracic
The document provides tips for teachers to promote professionalism, including making themselves available through involvement in speech communities, networking, and building an e-portfolio. It suggests teachers get involved by researching districts' needs, networking with contacts, maintaining professionalism, and having patience. Teachers are encouraged to put themselves and their ideas in play, continuously promote their brand, and share their potential through an online portfolio.
Blackwell Esteem Financials Pty Limited holds an Australian Financial Services License (Number 400364) to provide financial services. It is authorized to provide general financial product advice and deal in financial products such as derivatives, foreign exchange contracts, and securities. Peter James Varley is the auditor of the licensee. Blackwell Esteem Financials is a member of the Financial Ombudsman Service for external dispute resolution.
Jim Rohn argues that failure is not a single event, but rather the result of small errors in judgment repeated daily. These errors seem harmless at first, so people continue making them without realizing their cumulative negative impact. Success, on the other hand, comes from establishing a few simple daily disciplines. By developing disciplines like reading books or keeping a journal, people can start to foresee consequences and amend their thinking to avoid failure and achieve success.
In this ZENworks Configuration Management update new aspects of SP2 are being covered: 3rd party imaging with WinPE, ENGL 6.0 beta, experimental Windows 7 deployment and Software Packaging with AdminStudio Standard Edition
Sustaining & innovating amidst changes is the hallmark of exemplary leadership. Pelmar Group has been displaying this leadership for the last 50 years! In this special edition, we showcase for you Pelmar Eng Ltd and two other knowledge enhancing articles
This document discusses the political importance of algorithms and how they can reflect and amplify historical discrimination. It notes that control systems try to tightly control but if fully successful would have nothing left to control. Algorithms based on data like ZIP codes can reflect institutional discrimination. High-tech devices now use face recognition and target ads to specific genders. The document raises questions about how algorithms assemble subjects and regulate space through environmental determinism, and how algorithms are both ubiquitous through sensors but also fragile through hackability.
Sovereignty, Free Will, and Salvation - Limited AtonementRobin Schumacher
The document discusses the doctrine of limited atonement, which is the Calvinist view that Jesus's death was intended to save the elect alone, rather than all of humanity without exception. It provides biblical support for this view by noting that not all people will be saved, despite passages that say Christ died for the world, and that God must therefore limit the application of Christ's atonement. If the atonement applied to all people without exception, then all people would be saved. But the atonement is only effective for those who believe, which God sovereignly enables, so the atonement is limited in its application to the elect.
This document discusses the role of ethanol in preventing biofilm formation of the fungus Penicillium purpurogenum. Scanning electron microscopy showed that ethanol amended cultures exhibited a looser mycelial network compared to tight networks in control cultures, indicating ethanol decreased cell-cell and cell-surface adhesion. Experiments with glass, polystyrene, and tin strips found that ethanol amended cultures showed less adhesion on surfaces than control cultures. Biochemical assays demonstrated that ethanol induced oxidative stress in the fungus and decreased biomass, pigment production, and surface-bound proteins and exopolysaccharides. Therefore, ethanol can be used to control surface properties of fungi and inhibit biofilm formation.
1. This document contains schedules for examinations at Sekolah Kebangsaan Jalan Raja Syed Alwi in Kangar, Perlis.
2. It lists the subjects, times, and supervising teachers for each examination session on various dates in October and November 2015.
3. The schedules include examinations for all levels and cover subjects like Malay, English, Mathematics, Science, Islam, Music and Physical Education.
The children visit many places around Ireland, starting in Cobh Harbour where many Irish emigrants set sail for America during the potato famine. They see St. Colman's Cathedral and ships in the harbour. They meet a boy carrying salmon to Cork, and he offers to show them the city. In Cork they see St. Ann's Shandon church with its weather vane salmon. They learn about Saint Brigid and traditions from her day. Their tour continues to Kinsale, where they try traditional foods and see Desmond Castle. In Blarney they see people kissing the Blarney Stone, and in Killarney they watch Gaelic football and visit lakes and waterfalls. Their trip ends in Dingle,
The document discusses Hadoop infrastructure at TripAdvisor including:
1) TripAdvisor uses Hadoop across multiple clusters to analyze large amounts of data and power analytics jobs that were previously too large for a single machine.
2) They implement high availability for the Hadoop infrastructure including automatic failover of the NameNode using DRBD, Corosync and Pacemaker to replicate the NameNode across two servers.
3) Monitoring of the Hadoop clusters is done through Ganglia and Nagios to track hardware, jobs and identify issues. Regular backups of HDFS and Hive metadata are also performed for disaster recovery.
- Data is a precious resource that can last longer than the systems themselves (Tim Berners-Lee)
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It provides reliability, scalability and flexibility.
- Hadoop consists of HDFS for storage and MapReduce for processing. The main nodes include NameNode, DataNodes, JobTracker and TaskTrackers. Tools like Hive, Pig, HBase extend its capabilities for SQL-like queries, data flows and NoSQL access.
Top 10 lessons learned from deploying hadoop in a private cloudRogue Wave Software
This document discusses lessons learned from deploying Hadoop in a private cloud. Some key lessons include: choosing the right hardware with sufficient CPU, RAM, and bandwidth; understanding that configuration is critical for Hadoop, HBase, and Solr; expecting failures and bugs to occur; and realizing that big data projects take a long time to complete. Public clouds are expensive for long-term big data storage needs, so a private cloud may be more cost effective despite requiring infrastructure management. Open source tools like Hadoop have advanced to enable organizations to tackle "big data" challenges.
Hadoop and HBase make it easy to store terabytes of data, but how do you scale your search mechanism to sift through these mountains of bits and retrieve large result sets in a matter of milliseconds?
The Solr search server, based on Lucene, provides a scalable querying capability that nicely complements HBase. In this webinar, Rod Cope uses OpenLogic's production Solr and Hadoop environment as a case study on how you can handle rapid fire queries against terabytes of data, primarily through a combination of index sharding and fault-tolerant load balancing.
LinkedIn leverages the Apache Hadoop ecosystem for its big data analytics. Steady growth of the member base at LinkedIn along with their social activities results in exponential growth of the analytics infrastructure. Innovations in analytics tooling lead to heavier workloads on the clusters, which generate more data, which in turn encourage innovations in tooling and more workloads. Thus, the infrastructure remains under constant growth pressure. Heterogeneous environments embodied via a variety of hardware and diverse workloads make the task even more challenging.
This talk will tell the story of how we doubled our Hadoop infrastructure twice in the past two years.
• We will outline our main use cases and historical rates of cluster growth in multiple dimensions.
• We will focus on optimizations, configuration improvements, performance monitoring and architectural decisions we undertook to allow the infrastructure to keep pace with business needs.
• The topics include improvements in HDFS NameNode performance, and fine tuning of block report processing, the block balancer, and the namespace checkpointer.
• We will reveal a study on the optimal storage device for HDFS persistent journals (SATA vs. SAS vs. SSD vs. RAID).
• We will also describe Satellite Cluster project which allowed us to double the objects stored on one logical cluster by splitting an HDFS cluster into two partitions without the use of federation and practically no code changes.
• Finally, we will take a peek at our future goals, requirements, and growth perspectives.
SPEAKERS
Konstantin Shvachko, Sr Staff Software Engineer, LinkedIn
Erik Krogen, Senior Software Engineer, LinkedIn
Teradata Partners Conference Oct 2014 Big Data Anti-PatternsDouglas Moore
Douglas Moore discusses common anti-patterns seen when implementing big data solutions based on lessons learned from working with over 50 clients. He covers anti-patterns in hardware and infrastructure like relying on outdated reference architectures, tooling like trying to do analytics directly in NoSQL databases, and big data warehousing like over-curating data during ETL. The key is to understand the strengths and weaknesses of different tools and deploy the right solution for the intended workload.
Debugging Hive with Hadoop-in-the-Cloud by David Chaiken of AltiscaleData Con LA
Anyone who has used Hadoop knows that jobs sometimes get stuck. Hadoop is powerful, and it’s experiencing a tremendous rate of innovation, but it also has many rough edges. As Hadoop practitioners we all spend a lot of effort dealing with these rough edges in order to keep Hadoop and Hadoop jobs running well for our customers and/or organizations. For this session, we will look at a typical problem encountered by a Hadoop user, and discuss its implications for the future of Hadoop development. We will also go through the solution to this kind of problem using step-by-step instructions and the specific code we used to identify the issue. As a community, we need to work together to improve this kind of experience for our industry. Now that Hadoop 2 has been shipped, we believe the Hadoop community will be able to focus its energies on rounding off rough edges like these, and this session should provide advanced users with some tools and strategies to identify issues with jobs and how to keep these running smoothly.
Facing enterprise specific challenges – utility programming in hadoopfann wu
This document discusses managing large Hadoop clusters through various automation tools like SaltStack, Puppet, and Chef. It describes how to use SaltStack to remotely control and manage a Hadoop cluster. Puppet can be used to easily deploy Hadoop on hundreds of servers within an hour through Hadooppet. The document also covers Hadoop security concepts like Kerberos and folder permissions. It provides examples of monitoring tools like Ganglia, Nagios, and Splunk that can be used to track cluster metrics and debug issues. Common processes like datanode decommissioning and tools like the HBase Canary tool are also summarized. Lastly, it discusses testing Hadoop on AWS using EMR and techniques to reduce EMR costs
Innovation in the Data Warehouse - StampedeCon 2016StampedeCon
Enterprise Holding’s first started with Hadoop as a POC in 2013. Today, we have clusters on premises and in the cloud. This talk will explore our experience with Big Data and outline three common big data architectures (batch, lambda, and kappa). Then, we’ll dive into the decision points to necessary for your own cluster, for example: cloud vs on premises, physical vs virtual, workload, and security. These decisions will help you understand what direction to take. Finally, we’ll share some lessons learned with the pieces of our architecture worked well and rant about those which didn’t. No deep Hadoop knowledge is necessary, architect or executive level.
Cloudera Impala - Las Vegas Big Data Meetup Nov 5th 2014cdmaxime
Maxime Dumas gives a presentation on Cloudera Impala, which provides fast SQL query capability for Apache Hadoop. Impala allows for interactive queries on Hadoop data in seconds rather than minutes by using a native MPP query engine instead of MapReduce. It offers benefits like SQL support, improved performance of 3-4x up to 90x faster than MapReduce, and flexibility to query existing Hadoop data without needing to migrate or duplicate it. The latest release of Impala 2.0 includes new features like window functions, subqueries, and spilling joins and aggregations to disk when memory is exhausted.
Hive on Tez with LLAP (Late Loading Application) can achieve query processing speeds of over 100,000 queries per hour. Tuning various Hive and YARN parameters such as increasing the number of executor and I/O threads, memory allocation, and disabling consistent splits between LLAP daemons and data nodes was needed to reach this performance level on a test cluster of 45 nodes. Future work includes adding a web UI for monitoring LLAP clusters and implementing column-level access controls while allowing other frameworks like Spark to still access data through HiveServer2 and prevent direct access to HDFS for security reasons.
OSDC 2016 - Tuning Linux for your Database by Colin CharlesNETWAYS
Many operations folk know that performance varies depending on using one of the many Linux filesystems like EXT4 or XFS. They also know of the schedulers available, they see the OOM killer coming and more. However, appropriate configuration is necessary when you're running your databases at scale.
Learn best practices for Linux performance tuning for MariaDB/MySQL (where MyISAM uses the operating system cache, and InnoDB maintains its own aggressive buffer pool), as well as PostgreSQL and MongoDB (more dependent on the operating system). Topics that will be covered include: filesystems, swap and memory management, I/O scheduler settings, using and understanding the tools available (like iostat/vmstat/etc), practical kernel configuration, profiling your database, and using RAID and LVM.
There is a focus on bare metal as well as configuring your cloud instances in.
Learn from practical examples from the trenches.
If you also got the Big Data itch, here is something to ease the pain :-)
Answers to this questions will be available soon (more info in the attached link)
Which Big Data Appliance should YOU use?
(click on the attached link for Poll results)
Appliances are Small and Quick, Right?
Revealing the 6 Types of Big Data Appliances
Uncovering the Main Players
Challenges, Pitfalls, and Winning the Big Data Game
Where is all this leading YOU to?
You want to use MySQL in Amazon RDS, Rackspace Cloud, Google Cloud SQL or HP Helion Public Cloud? Check this out, from Percona Live London 2014. (Note that pricing of Google Cloud SQL changed prices on the same day after the presentation)
Bharath Mundlapudi presented on Disk Fail Inplace in Hadoop. He discussed how a single disk failure currently causes an entire node to be blacklisted. With newer hardware trends of more disks per node, this wastes significant resources. His team developed a Disk Fail Inplace approach where Hadoop can tolerate disk failures until a threshold. This included separating critical and user files, handling failures at startup and runtime in DataNode and TaskTracker, and rigorous testing of the new approach.
DrupalCampLA 2014 - Drupal backend performance and scalabilitycherryhillco
This document discusses various techniques for optimizing Drupal backend performance and scalability. It covers diagnosing issues through tools like Apache Benchmark and Munin, optimizing hardware, web and database servers like using Nginx, Varnish, MySQL tuning, and alternative databases like MongoDB. It also discusses PHP optimizations like opcode caching and HHVM. The goal is to provide strategies to handle more traffic, improve page response times, and minimize downtime through infrastructure improvements and code optimizations.
Big Data Developers Moscow Meetup 1 - sql on hadoopbddmoscow
This document summarizes a meetup about Big Data and SQL on Hadoop. The meetup included discussions on what Hadoop is, why SQL on Hadoop is useful, what Hive is, and introduced IBM's BigInsights software for running SQL on Hadoop with improved performance over other solutions. Key topics included HDFS file storage, MapReduce processing, Hive tables and metadata storage, and how BigInsights provides a massively parallel SQL engine instead of relying on MapReduce.
The document provides an introduction to Apache Hadoop, including:
1) It describes Hadoop's architecture which uses HDFS for distributed storage and MapReduce for distributed processing of large datasets across commodity clusters.
2) It explains that Hadoop solves issues of hardware failure and combining data through replication of data blocks and a simple MapReduce programming model.
3) It gives a brief history of Hadoop originating from Doug Cutting's Nutch project and the influence of Google's papers on distributed file systems and MapReduce.
Similar to Hadoop Robot from eBay at China Hadoop Summit 2015 (20)
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
5. Hadoop @eBay
•10+ large Hadoop Clusters
•10,000+ nodes
•50,000+ jobs per day
•50,000,000+ tasks per day
5
6. Shared vs Dedicated Clusters
• Shared clusters
• Used primarily for analytics of user behavior and inventory
• Mix of batch and ad-hoc jobs
• Mix of MR, YARN, Hive, Pig, Cascading, etc.
• Hadoop and HBase security enabled
• Dedicated clusters
• Very specific use case like index building
• Tight SLAs for jobs (in order of minutes)
• Immediate revenue impact
• Usually smaller than our shared clusters, but still large (600+ nodes)
6
9. Problem Statements
• Long trouble shooting time
• Bad cluster performance
• Too many different skus, operating systems and metadatas
• Human resource Cost
• Cluster Availability
9
10. Traditional Trouble Shooting Pipeline
Step 1
• Check failed application task logs to find out the suspicious hadoop nodes
Step 2
• Check the suspicious hadoop node hardware && system status
Step 3
• Check hadoop metrics and hadoop daemon logs
Step 4
• Check hadoop source code
10
11. Victim or Perpetrator ?
Sometimes you think you've found the perpetrators, however it may turn out to be
the victim.
11
12. What may impact cluster performance?
• Hardware
• System
• Hadoop
• JVM
• …
12
23. What is Hadoop Robot
Hadoop Robot is action and remediation center for eBay hadoop clusters:
• End-to-end automated remediation center
• API center for hadoop action and remediation
• Unified Hadoop Admin Console
• Real time maintenance view of Hadoop clusters
• Analytical insights into hardware maintenance data
23
24. End-to-end Automatic remediation center
•Hardware Maintenance
•Alert Detection
•Node Decommission
•Remediation
•Node Recommission
•Remove Failed Disk Volume
•Bad Disk Hot Swap
•Hadoop Daemon Restart
•Hadoop Abnormal Job Termination
•Hadoop Cluster Expansion
•…
24
32. One button hadoop installation and system configuration
We use ansible playbook to install and
configure various OS and software
packages including Hadoop.
32
Copy the Hadoop Packages,
Configuration and Codes from
Source to the Destination Node
Update Symbolic Links to point
to the Latest Hadoop Code.
Restart Hadoop Daemon
33. Bonnie++
•We use bonnie++ to carry out a stress-test of the repaired hardware. This not only
puts a load on the I/O and disk subsystem, but it also can flush out CPU, RAM
and fan/cooling issues.
33