Apache Tajo supports OpenStack Swift as one of its data sources.
This slide is presented at OpenStack Day in Korea 2015.
Outline
● Introduction to OpenStack Swift
● Introduction to Apache Tajo
● Tajo on Swift
● Demo
● Our Roadmap
This document discusses using artificial intelligence to optimize queries in BigQuery databases. It describes the benefits and limitations of managed databases like BigQuery. It then presents alternatives like SQL Server, ElasticSearch and Athena. The document outlines best practices for partitioning, clustering and limiting queries in BigQuery. It demonstrates how an AI optimization engine could predict query costs and perform real-time optimizations to scan less data and provide query recommendations. The goal is to make BigQuery faster, smarter and more efficient.
introduction to Neo4j (Tabriz Software Open Talks)Farzin Bagheri
This document provides an overview of Neo4j, a graph database. It begins with definitions of relational and NoSQL databases, categorizing NoSQL into key-value, document, column-oriented, and graph databases. Graph databases are explained to contain nodes, relationships, and properties. Neo4j is introduced as an example graph database, with Cypher listed as its query language. Examples of using Cypher to create nodes and relationships are provided. Finally, potential uses of Neo4j are listed, including social networks, network analysis, recommendations, and more.
Scylla Summit 2018: Kiwi.com Migration to Scylla - The Why, the How, the Fail...ScyllaDB
At Kiwi.com we never stop innovating our product and our architecture. Over the past couple of years, we saw a significant rise in technology requirements both globally and internally and had already tried several database solutions. The transformation went from small applications to complex microservices architectures. We first migrated to Cassandra from a big PostgreSQL cluster to get better performance and scalability, but our demands never stopped growing. That is why we decided to go with Scylla. In this talk, I will cover how our team approached testing of Scylla, the migration plan, how it impacts our business and how it influenced our high-level architecture of the application and infrastructure. It has a significant impact on disaster recovery and availability of our overall system.
The document describes Pinterest's scaling efforts from 2010 to 2012. It started with a single server on Rackspace and grew to use Amazon Web Services with over 100 web servers and database shards on MySQL, Redis, Memcache and other technologies. Key lessons included keeping systems simple initially and that clustering is difficult due to a single point of failure in the cluster management. Pinterest transitioned to manual sharding of MySQL databases to improve scalability while avoiding the complexity of clustering.
Zeotap: Moving to ScyllaDB - A Graph of Billions ScaleScyllaDB
Zeotap’s Connect product addresses the challenges of identity resolution and linking for AdTech and MarTech. Zeotap manages roughly 20 billion ID and growing. In their presentation, Zeotap engineers will delve into data access patterns, processing and storage requirements to make a case for a graph-based store. They will share the results of PoCs made on technologies such as D-graph, OrientDB, Aeropike and Scylla, present the reasoning for selecting JanusGraph backed by Scylla, and take a deep dive into their data model architecture from the point of ingestion. Learn what is required for the production setup, configuration and performance tuning to manage data at this scale.
Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. Neelesh Srinivas Salian offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way.
Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that expedite the process of getting started with Spark and transitioning from an ad hoc to a production workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix.
Neelesh shares Stitch Fix’s journey, exploring its ad hoc and production infrastructure and detailing its in-house tools and how they work in synergy with open source frameworks in a cloud environment. Neelesh also discusses the additional improvements to the infrastructure that help persist information for future use and optimization and explains how the implementation of Amazon’s EMR FS has helped make it easier to read from the S3 source.
This document discusses a command line tool called hotdog for interacting with DataDog. It summarizes that hotdog allows users to search for hosts on DataDog using tag expressions and instance IDs. It works by parsing expressions, retrieving host tag mappings from the DataDog API, building an index of host-tag relations, evaluating the expression against the index, and outputting results. The presenter then discusses how Treasure Data uses DataDog for monitoring and is hiring.
This document discusses using artificial intelligence to optimize queries in BigQuery databases. It describes the benefits and limitations of managed databases like BigQuery. It then presents alternatives like SQL Server, ElasticSearch and Athena. The document outlines best practices for partitioning, clustering and limiting queries in BigQuery. It demonstrates how an AI optimization engine could predict query costs and perform real-time optimizations to scan less data and provide query recommendations. The goal is to make BigQuery faster, smarter and more efficient.
introduction to Neo4j (Tabriz Software Open Talks)Farzin Bagheri
This document provides an overview of Neo4j, a graph database. It begins with definitions of relational and NoSQL databases, categorizing NoSQL into key-value, document, column-oriented, and graph databases. Graph databases are explained to contain nodes, relationships, and properties. Neo4j is introduced as an example graph database, with Cypher listed as its query language. Examples of using Cypher to create nodes and relationships are provided. Finally, potential uses of Neo4j are listed, including social networks, network analysis, recommendations, and more.
Scylla Summit 2018: Kiwi.com Migration to Scylla - The Why, the How, the Fail...ScyllaDB
At Kiwi.com we never stop innovating our product and our architecture. Over the past couple of years, we saw a significant rise in technology requirements both globally and internally and had already tried several database solutions. The transformation went from small applications to complex microservices architectures. We first migrated to Cassandra from a big PostgreSQL cluster to get better performance and scalability, but our demands never stopped growing. That is why we decided to go with Scylla. In this talk, I will cover how our team approached testing of Scylla, the migration plan, how it impacts our business and how it influenced our high-level architecture of the application and infrastructure. It has a significant impact on disaster recovery and availability of our overall system.
The document describes Pinterest's scaling efforts from 2010 to 2012. It started with a single server on Rackspace and grew to use Amazon Web Services with over 100 web servers and database shards on MySQL, Redis, Memcache and other technologies. Key lessons included keeping systems simple initially and that clustering is difficult due to a single point of failure in the cluster management. Pinterest transitioned to manual sharding of MySQL databases to improve scalability while avoiding the complexity of clustering.
Zeotap: Moving to ScyllaDB - A Graph of Billions ScaleScyllaDB
Zeotap’s Connect product addresses the challenges of identity resolution and linking for AdTech and MarTech. Zeotap manages roughly 20 billion ID and growing. In their presentation, Zeotap engineers will delve into data access patterns, processing and storage requirements to make a case for a graph-based store. They will share the results of PoCs made on technologies such as D-graph, OrientDB, Aeropike and Scylla, present the reasoning for selecting JanusGraph backed by Scylla, and take a deep dive into their data model architecture from the point of ingestion. Learn what is required for the production setup, configuration and performance tuning to manage data at this scale.
Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. Neelesh Srinivas Salian offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way.
Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that expedite the process of getting started with Spark and transitioning from an ad hoc to a production workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix.
Neelesh shares Stitch Fix’s journey, exploring its ad hoc and production infrastructure and detailing its in-house tools and how they work in synergy with open source frameworks in a cloud environment. Neelesh also discusses the additional improvements to the infrastructure that help persist information for future use and optimization and explains how the implementation of Amazon’s EMR FS has helped make it easier to read from the S3 source.
This document discusses a command line tool called hotdog for interacting with DataDog. It summarizes that hotdog allows users to search for hosts on DataDog using tag expressions and instance IDs. It works by parsing expressions, retrieving host tag mappings from the DataDog API, building an index of host-tag relations, evaluating the expression against the index, and outputting results. The presenter then discusses how Treasure Data uses DataDog for monitoring and is hiring.
This document discusses using Elasticsearch as a time series database. It covers why Elasticsearch was chosen over other options for storing metrics from the open source performance monitoring tool Stagemonitor. The document discusses Elasticsearch's ability to scale, its functions and visualization support in Kibana. It also covers how Stagemonitor's data is modeled in Elasticsearch, including the use of tags, and how index management is handled through a hot/cold node architecture and tools like Curator.
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...Fwdays
We will start from understanding how Real-Time Analytics can be implemented on Enterprise Level Infrastructure and will go to details and discover how different cases of business intelligence be used in real-time on streaming data. We will cover different Stream Data Processing Architectures and discus their benefits and disadvantages. I'll show with live demos how to build Fast Data Platform in Azure Cloud using open source projects: Apache Kafka, Apache Cassandra, Mesos. Also I'll show examples and code from real projects.
This document summarizes Ryu Kobayashi's presentation on HDP2 and YARN operations. The presentation introduced YARN, the resource management framework in Hadoop 2.0, describing its architecture and how it differs from the previous MapReduce v1 framework. It highlighted important considerations for YARN resource management and potential bugs in older versions of Hadoop.
When learning Apache Spark, where should a person begin? What are the key fundamentals when learning Apache Spark? Resilient Distributed Datasets, Spark Drivers and Context, Transformations, Actions.
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
Scylla Summit 2018: Scaling your time series data with NewtsScyllaDB
Today's datasets are growing at an exponential rate. Collection, storage, analysis, and reporting are becoming more challenging, and the results more valued. A decade ago, RRDTool's algorithms were well-suited to our requirements, but they fall short of scaling to current demands. A new direction is needed, one that prioritizes write-optimized storage, and that scales beyond a single host.
This presentation will provide an overview of Newts, a distributed time-series data store based on ScyllaDB, show how it compares to other solutions, and take a look at how it is integrated in OpenNMS.
Data-Driven Development Era and Its TechnologiesSATOSHI TAGOMORI
This document discusses data-driven development and the technologies used in the data analytics process. It covers topics like data collection, storage, processing, and visualization. The document advocates using managed cloud services for data and analytics to focus on data instead of managing infrastructure. Choosing technologies should be based on the type of data and problems to solve, not the other way around. Services like Google BigQuery, Amazon Redshift, and Treasure Data are recommended for their ease of use.
This document provides information on and demonstrations of several bleeding edge database technologies: Aerospike, Algebraix Data, and Google BigQuery. It includes benchmark results, architecture diagrams, pricing and deployment details for each one. Example use cases and instructions for getting started with the technologies are also provided.
Short overview of data infrastructure at Bazaarvoice. We use a combination of many different data stores such as MySQL, SOLR, Infobright, MongoDB and Hadoop.
Amazon aws big data demystified | Introduction to streaming and messaging flu...Omid Vahdaty
This document provides an overview of streaming data and messaging concepts including batch processing, streaming, streaming vs messaging, challenges with streaming data, and AWS services for streaming and messaging like Kinesis, Kinesis Firehose, SQS, and Kafka. It discusses use cases and comparisons for these different services. For example, Kinesis is suitable for complex analytics on streaming data while SQS focuses on per-event messaging. Firehose automatically loads streaming data into AWS services like S3 and Redshift without custom coding.
SQL, NoSQL, Distributed SQL: Choose your DataStore carefullyMd Kamaruzzaman
In modern Software Development and Software Architecture, selecting the right DataStore is one of the most challenging and important task. In this presentation, I have summarized the major DataStores and the decision criteria to select the right DataStore according to the use case.
GPS Insight on Using Presto with Scylla for Data Analytics and Data ArchivalScyllaDB
GPS Insight is a leader in fleet vehicle management using IoT. Internally they use a combination of SQL and NoSQL big data technologies, including distributed SQL data analytics via Presto, an open-source query engine developed by Facebook. Learn how to set up, configure, and use Presto with Scylla for supporting ad hoc non-partition key queries for analytics and data scientists. Plus hear how to use Presto for a Data Archival approach with csv files on S3 or similar storage appliance.
ManetoDB: Key/Value storage, BigData in Open Stack_Сергей Ковалев, Илья СвиридовGeeksLab Odessa
MagnetoDB is an open source implementation of the Amazon DynamoDB key-value database API for OpenStack. It provides a scalable noSQL database with a schemaless data model and predictable performance. The current version supports basic CRUD operations and data querying. Future work includes adding additional DynamoDB API features and integrating further with OpenStack services. MagnetoDB aims to allow applications using DynamoDB to run on OpenStack.
Presto is Uber's distributed SQL query engine for their Hadoop data warehouse. Some key points:
- Presto allows interactive SQL queries directly on Uber's petabyte-scale Hadoop data lake without needing to first load the data into another database.
- It provides fast performance at scale by leveraging columnar data formats like Parquet and optimizing for distributed execution across many nodes.
- Uber deployed a 200 node Presto cluster that handles 30,000 queries per day, serving both ad hoc queries and real-time applications accessing data in Hadoop and improving on the performance of alternative solutions like Hive.
Open source big data landscape and possible ITS applicationsSoftwareMill
What is big data, and how open-source big data projects, such as Apache Spark, Kafka and Cassandra can be used in ITS (Intelligent Transport Systems) related projects.
MongoDB uses replication to provide high availability and redundancy. The document discusses MongoDB replication fundamentals including replica sets, oplogs, and reading from secondary nodes. It provides an overview of primary/secondary roles in replica sets, how writes are logged to oplogs, and how secondaries replicate by reading the primary's oplog. It also covers read preference settings and write concerns in MongoDB replication.
This document discusses Presto, an open source distributed SQL query engine. It is used by many large companies like Facebook, Uber, and Netflix for querying large datasets across various data sources. Presto provides high performance through its columnar processing, runtime compilation, and new cost-based optimizer. The document also describes how Presto can be run on AWS and Azure cloud platforms through partnerships with Starburst, who contributed many features to Presto and provides commercial support for enterprises.
How ReversingLabs Serves File Reputation Service for 10B FilesScyllaDB
ReversingLabs is on a mission to deliver threat intelligence to their users by providing complete visibility and insight into every destructive object. To deliver on their commitment, they migrated to Scylla to handle thousands of updates per second in their processing engines. In their talk, they will go over their requirements and show how they tuned the system to handle requests from their API frontend.
AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive Omid Vahdaty
This document provides an overview of various AWS big data services including Athena, Redshift Spectrum, EMR, and Hive. It discusses how Athena allows users to run SQL queries directly on data stored in S3 using Presto. Redshift Spectrum enables querying data in S3 using standard SQL from Amazon Redshift. EMR is a managed Hadoop framework that can run Hive, Spark, and other big data applications. Hive provides a SQL-like interface to query data stored in various formats like Parquet and ORC on distributed storage systems. The document demonstrates features and provides best practices for working with these AWS big data services.
This document discusses using Elasticsearch as a time series database. It covers why Elasticsearch was chosen over other options for storing metrics from the open source performance monitoring tool Stagemonitor. The document discusses Elasticsearch's ability to scale, its functions and visualization support in Kibana. It also covers how Stagemonitor's data is modeled in Elasticsearch, including the use of tags, and how index management is handled through a hot/cold node architecture and tools like Curator.
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...Fwdays
We will start from understanding how Real-Time Analytics can be implemented on Enterprise Level Infrastructure and will go to details and discover how different cases of business intelligence be used in real-time on streaming data. We will cover different Stream Data Processing Architectures and discus their benefits and disadvantages. I'll show with live demos how to build Fast Data Platform in Azure Cloud using open source projects: Apache Kafka, Apache Cassandra, Mesos. Also I'll show examples and code from real projects.
This document summarizes Ryu Kobayashi's presentation on HDP2 and YARN operations. The presentation introduced YARN, the resource management framework in Hadoop 2.0, describing its architecture and how it differs from the previous MapReduce v1 framework. It highlighted important considerations for YARN resource management and potential bugs in older versions of Hadoop.
When learning Apache Spark, where should a person begin? What are the key fundamentals when learning Apache Spark? Resilient Distributed Datasets, Spark Drivers and Context, Transformations, Actions.
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
Scylla Summit 2018: Scaling your time series data with NewtsScyllaDB
Today's datasets are growing at an exponential rate. Collection, storage, analysis, and reporting are becoming more challenging, and the results more valued. A decade ago, RRDTool's algorithms were well-suited to our requirements, but they fall short of scaling to current demands. A new direction is needed, one that prioritizes write-optimized storage, and that scales beyond a single host.
This presentation will provide an overview of Newts, a distributed time-series data store based on ScyllaDB, show how it compares to other solutions, and take a look at how it is integrated in OpenNMS.
Data-Driven Development Era and Its TechnologiesSATOSHI TAGOMORI
This document discusses data-driven development and the technologies used in the data analytics process. It covers topics like data collection, storage, processing, and visualization. The document advocates using managed cloud services for data and analytics to focus on data instead of managing infrastructure. Choosing technologies should be based on the type of data and problems to solve, not the other way around. Services like Google BigQuery, Amazon Redshift, and Treasure Data are recommended for their ease of use.
This document provides information on and demonstrations of several bleeding edge database technologies: Aerospike, Algebraix Data, and Google BigQuery. It includes benchmark results, architecture diagrams, pricing and deployment details for each one. Example use cases and instructions for getting started with the technologies are also provided.
Short overview of data infrastructure at Bazaarvoice. We use a combination of many different data stores such as MySQL, SOLR, Infobright, MongoDB and Hadoop.
Amazon aws big data demystified | Introduction to streaming and messaging flu...Omid Vahdaty
This document provides an overview of streaming data and messaging concepts including batch processing, streaming, streaming vs messaging, challenges with streaming data, and AWS services for streaming and messaging like Kinesis, Kinesis Firehose, SQS, and Kafka. It discusses use cases and comparisons for these different services. For example, Kinesis is suitable for complex analytics on streaming data while SQS focuses on per-event messaging. Firehose automatically loads streaming data into AWS services like S3 and Redshift without custom coding.
SQL, NoSQL, Distributed SQL: Choose your DataStore carefullyMd Kamaruzzaman
In modern Software Development and Software Architecture, selecting the right DataStore is one of the most challenging and important task. In this presentation, I have summarized the major DataStores and the decision criteria to select the right DataStore according to the use case.
GPS Insight on Using Presto with Scylla for Data Analytics and Data ArchivalScyllaDB
GPS Insight is a leader in fleet vehicle management using IoT. Internally they use a combination of SQL and NoSQL big data technologies, including distributed SQL data analytics via Presto, an open-source query engine developed by Facebook. Learn how to set up, configure, and use Presto with Scylla for supporting ad hoc non-partition key queries for analytics and data scientists. Plus hear how to use Presto for a Data Archival approach with csv files on S3 or similar storage appliance.
ManetoDB: Key/Value storage, BigData in Open Stack_Сергей Ковалев, Илья СвиридовGeeksLab Odessa
MagnetoDB is an open source implementation of the Amazon DynamoDB key-value database API for OpenStack. It provides a scalable noSQL database with a schemaless data model and predictable performance. The current version supports basic CRUD operations and data querying. Future work includes adding additional DynamoDB API features and integrating further with OpenStack services. MagnetoDB aims to allow applications using DynamoDB to run on OpenStack.
Presto is Uber's distributed SQL query engine for their Hadoop data warehouse. Some key points:
- Presto allows interactive SQL queries directly on Uber's petabyte-scale Hadoop data lake without needing to first load the data into another database.
- It provides fast performance at scale by leveraging columnar data formats like Parquet and optimizing for distributed execution across many nodes.
- Uber deployed a 200 node Presto cluster that handles 30,000 queries per day, serving both ad hoc queries and real-time applications accessing data in Hadoop and improving on the performance of alternative solutions like Hive.
Open source big data landscape and possible ITS applicationsSoftwareMill
What is big data, and how open-source big data projects, such as Apache Spark, Kafka and Cassandra can be used in ITS (Intelligent Transport Systems) related projects.
MongoDB uses replication to provide high availability and redundancy. The document discusses MongoDB replication fundamentals including replica sets, oplogs, and reading from secondary nodes. It provides an overview of primary/secondary roles in replica sets, how writes are logged to oplogs, and how secondaries replicate by reading the primary's oplog. It also covers read preference settings and write concerns in MongoDB replication.
This document discusses Presto, an open source distributed SQL query engine. It is used by many large companies like Facebook, Uber, and Netflix for querying large datasets across various data sources. Presto provides high performance through its columnar processing, runtime compilation, and new cost-based optimizer. The document also describes how Presto can be run on AWS and Azure cloud platforms through partnerships with Starburst, who contributed many features to Presto and provides commercial support for enterprises.
How ReversingLabs Serves File Reputation Service for 10B FilesScyllaDB
ReversingLabs is on a mission to deliver threat intelligence to their users by providing complete visibility and insight into every destructive object. To deliver on their commitment, they migrated to Scylla to handle thousands of updates per second in their processing engines. In their talk, they will go over their requirements and show how they tuned the system to handle requests from their API frontend.
AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive Omid Vahdaty
This document provides an overview of various AWS big data services including Athena, Redshift Spectrum, EMR, and Hive. It discusses how Athena allows users to run SQL queries directly on data stored in S3 using Presto. Redshift Spectrum enables querying data in S3 using standard SQL from Amazon Redshift. EMR is a managed Hadoop framework that can run Hive, Spark, and other big data applications. Hive provides a SQL-like interface to query data stored in various formats like Parquet and ORC on distributed storage systems. The document demonstrates features and provides best practices for working with these AWS big data services.
Vida Sencilla is a client-based company that provides creative and technical support services for entertainment and corporate events in Spain. They offer a network of suppliers and personnel to help plan all aspects of events from concept to execution. With over 30 years of experience in event production, they can provide services such as talent booking, equipment rental, staging and production management.
Apache Tajo on Swift: Bringing SQL to the OpenStack WorldJihoon Son
This slide was presented at the SK Telecom T Developer Forum. It contains the brief evaluation results of the query execution performance of Tajo on Swift.
I conducted two kinds of experiments; The first experiment was to compare the performance of Tajo with on another distributed storage, i.e., HDFS. And the second experiment was the scalability test of Swift.
Interestingly, the scan performance on Swift is slower more than two times than that on HDFS. In addition, the task scheduling time on Swift is much greater than that on HDFS, which means the query initialization cost is very high.
This document discusses basic configurations in Apache Tajo 0.11, including cluster resources, concurrent disk access, and garbage collection. It recommends configuring the worker heap size, number of disks per node, minimum memory per task, number of tasks assigned per disk, and temporary directory locations. The document also notes that Tajo works well with default configurations and provides links for more information.
The document provides an overview of serverless computing and deploying Python applications using AWS Lambda. It discusses how serverless computing removes the need to manage servers and allows scaling without capacity planning. The rest of the document demonstrates how to deploy a Python application on AWS Lambda using the Zappa framework. It shows how Zappa handles packaging code and dependencies, deployment, and management of Lambda functions and API Gateway configuration. Some potential issues with serverless like cold starts and limitations on function duration are also covered.
Savanna is an OpenStack component that allows elastic provisioning of Hadoop clusters in OpenStack. It has a 3 phase roadmap - phase 1 allows basic cluster provisioning which is complete, phase 2 will add advanced configuration and tool integration currently in progress, and phase 3 will enable analytics as a service with a job execution framework. Savanna uses an extensible plugin architecture to provision Hadoop VMs and configure the clusters, integrating with other OpenStack components like Nova, Glance, and Swift.
Out of the box, Accumulo's strengths are difficult to appreciate without first building an application that showcases its capabilities to handle massive amounts of data. Unfortunately, building such an application is non-trivial for many would-be users, which affects Accumulo's adoption.
In this talk, we introduce Datawave, a complete ingest, query, and analytic framework for Accumulo. Datawave, recently open-sourced by the National Security Agency, capitalizes on Accumulo's capabilities, provides an API for working with structured and unstructured data, and boasts a robust, flexible, and scalable backend.
We'll do a deep dive into Datawave's project layout, table structures, and APIs in addition to demonstrating the Datawave quickstart—a tool that makes it incredibly easy to hit the ground running with Accumulo and Datawave without having to develop a complete application.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2lGNybu.
Stefan Krawczyk discusses how his team at StitchFix use the cloud to enable over 80 data scientists to be productive. He also talks about prototyping ideas, algorithms and analyses, how they set up & keep schemas in sync between Hive, Presto, Redshift & Spark and make access easy for their data scientists, etc. Filmed at qconsf.com..
Stefan Krawczyk is Algo Dev Platform Lead at StitchFix, where he’s leading development of the algorithm development platform. He spent formative years at Stanford, LinkedIn, Nextdoor & Idibon, working on everything from growth engineering, product engineering, data engineering, to recommendation systems, NLP, data science and business intelligence.
This document discusses support technologies for development infrastructure in Vietnam. It introduces provisioning tools, virtual machines, and other serverside technologies that can help build and maintain development environments more efficiently. Specifically, it covers:
- Provisioning tools like Chef and Ansible that allow infrastructure to be coded and reproduced automatically.
- Virtual machines like Vagrant and Docker that provide isolated environments for applications without the overhead of full virtual machines.
- Other technologies like chatbots and continuous integration tools that can enhance development processes.
In conclusion, these technologies allow infrastructure maintenance to be less tiresome and costly when used to automate environment setup and testing.
This document provides an overview of serverless computing using AWS Lambda. It begins with defining serverless architecture and its benefits over traditional server-based architectures like reduced maintenance and pay-per-use model. It then demonstrates how to write and deploy Python applications on AWS Lambda using the Zappa framework, covering choosing templates and triggers, adding configuration and code, testing, and deployment. Some pitfalls of the serverless model like cold starts and limits are also discussed. Alternatives to AWS Lambda like Google Cloud Functions and Azure Functions are briefly mentioned.
Accelerating analytics in the cloud with the Starburst Presto + Alluxio stackAlluxio, Inc.
Alluxio Tech Talk
January 21, 2020
Speakers:
Matt Fuller, Starburst
Dipti Borkar, Alluxio
With the advent of the public clouds and data increasingly siloed across many locations -- on premises and in the public cloud -- enterprises are looking for more flexibility and higher performance approaches to analyze their structured data.
Join us for this tech talk where we’ll introduce the Starburst Presto, Alluxio, and cloud object store stack for building a highly-concurrent and low-latency analytics platform. This stack provides a strong solution to run fast SQL across multiple storage systems including HDFS, S3, and others in public cloud, hybrid cloud, and multi-cloud environments. You’ll learn more about:
- The architecture of Presto, an open source distributed SQL engine
- How the Presto + Alluxio stack queries data from cloud object storage like S3 for faster and more cost-effective analytics
- Achieving data locality and cross-job caching with Alluxio regardless of where data is persisted
10,000 microservices are generated each month using JHipster!
During this in-depth session by the two JHipster lead developers, we’ll detail:
How to develop and deploy microservices easily
Scalability and failover of microservices
The JHipster Registry for scaling, configuring and monitoring microservices
Common architecture patterns and pitfalls
The document summarizes Spring Data Neo4j 4.0, a new version of the Spring Data project that provides integration with the Neo4j graph database. It describes Neo4j and Spring Data briefly, then outlines the key features and architecture of SDN 4.0, including a standalone object-graph mapping layer, variable depth persistence, and integration with Spring and repositories. It demonstrates a sample conference application built with SDN 4.0 and provides information on getting started and support resources.
This document discusses Twitter's adoption of open source technologies and how it is evolving with extending infrastructure support for the cloud. It provides details on Twitter's use of open source technologies like Apache Hadoop, Apache Spark and Apache Kafka at scale for data processing, storage and analytics. It also discusses Twitter's cloud journey, challenges in areas like metadata integration, data replication at scale, security and tooling for easy onboarding of cloud services. Lastly, it covers topics like a focus on standards, challenges of multi-cloud, and whether to choose all cloud or a hybrid approach going forward.
This document provides a summary of Netflix's architecture and use of open source software. It discusses:
- Why Netflix open sources software, including gathering feedback, collaboration, and improving retention and recruiting
- Popular Netflix open source projects like Eureka, Ribbon, and Hystrix that are widely used in cloud architectures
- Netflix's microservices architecture and emphasis on automation, high availability, and continuous delivery
- How Netflix ensures operational visibility and security at scale through open source tools like Turbine, Atlas, and Security Monkey
- Getting started resources for understanding and running Netflix's technologies like ZeroToCloud and ZeroToDocker workshops
This document provides an overview of serverless computing using AWS Lambda. It discusses what serverless means, how it addresses issues with traditional server-based architectures like capacity planning and scaling. It then covers how to build and deploy serverless Python applications using AWS Lambda, including choosing templates and triggers, adding configuration and code, testing, and writing clients. Alternatives like Google Cloud Functions and the Zappa framework for deploying serverless apps are also mentioned.
CON6423: Scalable JavaScript applications with Project NashornMichel Graciano
In the age of cloud computing and highly demanding systems, some new approaches for application architectures such as the event-driven model have been proposed and successfully implemented with Node.js. With the Nashorn JavaScript engine, it is possible to run JavaScript applications directly in the JVM, enabling access to the latest Node.js frameworks while taking advantage of the Java platform’s scalability, manageability, tools, and extensive collection of Java libraries and middleware. This session demonstrates how to use Nashorn to create highly scalable JavaScript applications leveraging the full power of the JVM by using the projects Avatar and Node.js with Avatar.js and Vert.x, highlighting their key benefits, issues, and challenges.
Visual, scalable, and manageable data loading to and from Neo4j with Apache Hop Neo4j
This document discusses Apache Hop, an open source data orchestration platform. It provides an overview of Apache Hop's capabilities for managing data pipelines and workflows. Key features highlighted include its modular architecture, support for technologies like Apache Spark and Neo4j, and focus on ease of use, testing, and community development. The roadmap outlines plans to graduate to a top-level Apache project and improve cloud and mobile support.
Hadoop on OpenStack - Sahara @DevNation 2014spinningmatt
This document provides an overview of Sahara, an OpenStack project that aims to simplify managing Hadoop infrastructure and tools. Sahara allows users to create and manage Hadoop clusters through a programmatic API or web console. It uses a plugin architecture where Hadoop distribution vendors can integrate their management software. Currently there are plugins for vanilla Apache Hadoop, Hortonworks Data Platform, and Intel Distribution for Apache Hadoop. The document outlines Sahara's architecture, APIs, roadmap, and demonstrates its use through a live demo analyzing transaction data with the BigPetStore sample application on Hadoop.
Those are slides from Dev.IL meetup talk, by Or Rosenblatt & Yshay Yaacobi from Soluto RND
https://www.meetup.com/Dev-IL/events/253252917/
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You developed a cool java infrastructure for your team.
Your team then shifts to python, so you rewrite the utility in python.
Then the team next door asks you to do the same rewrite for their node/typescript service.
You ask for a raise and write it again in typescript.
Now your colleague reads in HackerNews about the next cool trending language in the block.
Ain’t nobody got time for that!!!
Join us to hear how the powerful combination sidecar pattern and Kubernetes can help you solve this issue by allowing different services to use the same utility, regardless of stack or language.
You will become stack-free forever!
This document discusses using Azure DevOps and Snowflake to enable continuous integration and continuous deployment (CI/CD) of database changes. It covers setting up source control in a repository, implementing pull requests for code reviews, building deployment artifacts in a build pipeline, and deploying artifacts to development, test, and production environments through a release pipeline. The document also highlights key Snowflake features like zero-copy cloning that enable testing deployments before production.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Software Engineering and Project Management - Introduction, Modeling Concepts...
Apache Tajo on Swift
1. Apache Tajo on Swift
Bringing SQL to the OpenStack World
Jihoon Son
Apache Tajo PMC member
2. Who am I
● Jihoon Son
○ Ph.D candidate (Computer Science & Engineering,
2010.3 ~)
○ Apache Tajo PMC and Committer (2014.5.1 ~)
○ Mentor of Google Summer of Code (2013)
● Contacts
○ Email: jihoonson AT apache.org
○ LinkedIn: https://www.linkedin.com/in/jihoonson
4. OpenStack Swift
● Popular object storage
○ Images, videos, logs, ...
● Enterprises store objects on Swift to provide
their services
○ Usually private clusters
5. SQL on Swift
● Data analysis is important to improve the quality
of their services
○ SQL is one of the most powerful and popular query
language
● Many enterprise data analysis tools relying on
SQL
○ OLAP, visualization, data mining, …
● Need for using SQL on Swift
6. Apache Tajo
● Scalable, efficient, and fault-tolerant data
warehouse system
○ Support SQL standards compliance
○ Efficient batch execution and interactive ad-hoc
analysis
■ Low latency and high throughput
■ No use of MapReduce
○ No single point of failure
7. Apache Tajo
● Active open source project
○ 18 committers and 16 contributors
○ Activity summary
9. Tajo on Swift
Pluggable Storage Layer
MasterMasterTajo
Master
Tajo
Worker
Tajo
Worker
Tajo
Worker
Tajo
Worker
...
...
Swift
10. Tajo on Swift
● No need to modify code of Tajo and Swift
○ Tajo can access Swift with the Hadoop-openstack
library
■ But, doesn’t need to install or run Hadoop
○ Just use it
Swift
Network
11. Tajo on Swift
● Configuration highlights
○ Swift configuration
■ Need the keystone authentication for the Hadoop
■ No additional configurations
○ HDFS configuration
■ Different cloud providers support
● Key name pattern
fs.swift.service.${provider}
12. Tajo on Swift
● Configuration highlights
○ Swift configuration
■ Need the keystone authentication for the HDFS client
■ No additional configurations
○ HDFS configuration
■ Different cloud providers support
● Key name pattern
fs.swift.service.${provider}
13. Tajo on Swift
● Data locality problem
Worker
Storage
Node
Interconnection Network
Node A
Worker
Node B
Storage
Node
Significant
Network
Overhead
14. Tajo on Swift
● Data locality problem
Worker
Storage
Node
Interconnection Network
Node A
Worker
Node B
Storage
Node
15. Advanced Integration
● List endpoints middleware
○ Providing the location information of objects,
accounts or containers
■ Tajo workers can directly access each object
○ Example
17. Advanced Integration
● Location-aware computing
○ Moving the processing close to the data
■ Avoiding the performance degradation due to the data
transfer over the network
○ Important issue when Tajo and Swift share the same
cluster