Future of data visualization
•Download as PPTX, PDF•
0 likes•655 views
Apache Zeppelin is an emerging open-source tool for data visualization that allows for interactive data analytics. It provides a web-based notebook interface that allows users to write and execute code in languages like SQL and Scala. The tool offers features like built-in visualization capabilities, pivot tables, dynamic forms, and collaboration tools. Zeppelin works with backends like Apache Spark and uses interpreters to connect to different data processing systems. It is predicted to influence big data visualization in the coming years.
Report
Share
Report
Share
Recommended
Using Visualization to Succeed with Big Data
The document summarizes a webinar on big data visualization. It discusses drivers for the big data visualization market and new tools emerging. It then profiles several major vendors that offer big data visualization solutions, including Microsoft, QlikView, TIBCO, Tableau, Platfora, Datameer, Splunk, Jaspersoft, and Alpine Data. It concludes with an overview of how Pactera can help clients build advanced analytics solutions.
Big Data visualization with Apache Spark and Zeppelin
This presentation gives an overview of Apache Spark and explains the features of Apache Zeppelin(incubator). Zeppelin is the open source tool for data discovery, exploration and visualization. It supports REPLs for shell, SparkSQL, Spark(scala), python and angular. This presentation was made on the Big Data Day, at the Great Indian Developer Summit, Bangalore, April 2015
Lambda architecture: from zero to One
Story of architecture evolution of one project from zero to Lambda Architecture. Also includes information on how we scaled cluster as soon as architecture is set up.
Contains nice performance charts after every architecture change.
Spark Summit EU talk by Stephan Kessler
This document summarizes a talk given by Stephan Kessler at the Spark Summit Europe 2016 about integrating business functionality and specialized engines into Apache Spark using SAP HANA Vora. Key points discussed include using currency conversion and time series query capabilities directly in Spark by pushing computations to the relevant data sources via Spark extensions. SAP HANA Vora allows moving parts of the Spark logical query plan to various data sources like HANA, graph and document stores to perform analysis close to the data.
Large Scale Lakehouse Implementation Using Structured Streaming
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
Interactive Visualization of Streaming Data Powered by Spark by Ruhollah Farc...
This document discusses how to visualize streaming data using Spark. It describes how Spark Streaming can be used to process streaming data in real-time and integrate it with visualization tools. Key points include:
- Spark Streaming receives streaming data from sources like Kafka and processes it using in-memory computations in a single JVM cluster.
- The processed data can be stored in buffers like MongoDB or output to systems like MemSQL, Solr to enable interactive visualizations that update in real-time.
- A demo is shown of Twitter data being streamed and analyzed using Spark Streaming with results stored in MemSQL and Solr for visualization.
- Benefits of this approach include being able to work with streaming data
Lambda architecture with Spark
Presentation on the struggles with traditional architectures and an overview of the Lambda Architecture utilizing Spark to drive massive amounts of both batch and streaming data for processing and analytics
Deep Dive into the New Features of Apache Spark 3.1
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.1 extends its scope with more than 1500 resolved JIRAs. We will talk about the exciting new developments in the Apache Spark 3.1 as well as some other major initiatives that are coming in the future. In this talk, we want to share with the community many of the more important changes with the examples and demos.
The following features are covered: the SQL features for ANSI SQL compliance, new streaming features, and Python usability improvements, the performance enhancements and new tuning tricks in query compiler.
Recommended
Using Visualization to Succeed with Big Data
The document summarizes a webinar on big data visualization. It discusses drivers for the big data visualization market and new tools emerging. It then profiles several major vendors that offer big data visualization solutions, including Microsoft, QlikView, TIBCO, Tableau, Platfora, Datameer, Splunk, Jaspersoft, and Alpine Data. It concludes with an overview of how Pactera can help clients build advanced analytics solutions.
Big Data visualization with Apache Spark and Zeppelin
This presentation gives an overview of Apache Spark and explains the features of Apache Zeppelin(incubator). Zeppelin is the open source tool for data discovery, exploration and visualization. It supports REPLs for shell, SparkSQL, Spark(scala), python and angular. This presentation was made on the Big Data Day, at the Great Indian Developer Summit, Bangalore, April 2015
Lambda architecture: from zero to One
Story of architecture evolution of one project from zero to Lambda Architecture. Also includes information on how we scaled cluster as soon as architecture is set up.
Contains nice performance charts after every architecture change.
Spark Summit EU talk by Stephan Kessler
This document summarizes a talk given by Stephan Kessler at the Spark Summit Europe 2016 about integrating business functionality and specialized engines into Apache Spark using SAP HANA Vora. Key points discussed include using currency conversion and time series query capabilities directly in Spark by pushing computations to the relevant data sources via Spark extensions. SAP HANA Vora allows moving parts of the Spark logical query plan to various data sources like HANA, graph and document stores to perform analysis close to the data.
Large Scale Lakehouse Implementation Using Structured Streaming
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
Interactive Visualization of Streaming Data Powered by Spark by Ruhollah Farc...
This document discusses how to visualize streaming data using Spark. It describes how Spark Streaming can be used to process streaming data in real-time and integrate it with visualization tools. Key points include:
- Spark Streaming receives streaming data from sources like Kafka and processes it using in-memory computations in a single JVM cluster.
- The processed data can be stored in buffers like MongoDB or output to systems like MemSQL, Solr to enable interactive visualizations that update in real-time.
- A demo is shown of Twitter data being streamed and analyzed using Spark Streaming with results stored in MemSQL and Solr for visualization.
- Benefits of this approach include being able to work with streaming data
Lambda architecture with Spark
Presentation on the struggles with traditional architectures and an overview of the Lambda Architecture utilizing Spark to drive massive amounts of both batch and streaming data for processing and analytics
Deep Dive into the New Features of Apache Spark 3.1
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.1 extends its scope with more than 1500 resolved JIRAs. We will talk about the exciting new developments in the Apache Spark 3.1 as well as some other major initiatives that are coming in the future. In this talk, we want to share with the community many of the more important changes with the examples and demos.
The following features are covered: the SQL features for ANSI SQL compliance, new streaming features, and Python usability improvements, the performance enhancements and new tuning tricks in query compiler.
Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, L...
Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, L...TUMRA | Big Data Science - Gain a competitive advantage through Big Data & Data Science
This document provides a summary of using Apache Spark for continuous analytics and optimization. It discusses using Spark for collecting data from various sources, processing the data using Spark's capabilities for streaming, machine learning and SQL queries, and reporting insights. An example use case is presented for social media analysis using Spark Streaming to process a real-time data stream from Kafka and analyze the data using both the Spark SQL and core Spark APIs in Scala.Mining public datasets using opensource tools: Zeppelin, Spark and Juju
There are plenty of public datasets out there available and the number is growing. Few recent and most useful of BigData ecosystem tools are showcased: Apache Zeppelin (incubating), Apache Spark and Juju.
Infrastructure for Deep Learning in Apache Spark
In machine learning projects, the preparation of large datasets is a key phase which can be complex and expensive. It was traditionally done by data engineers before the handover to data scientists or ML engineers. They operated in different environments due to the differences in the tools, frameworks and runtimes required in each phase. Spark's support for different types of workloads brought data engineering closer to the downstream activities like machine learning that depended on the data. Unifying data acquisition, preprocessing, training models and batch inferencing under a single platform enabled by Spark not only provided seamless experience between different phases and helped accelerate the end-to-end ML lifecycle but also lowered the TCO in the building, managing the infrastructure to cover different phases. With that, the needs of a shared infrastructure expanded to include specialized hardware like GPUs and support deep learning workloads as well. Spark can effectively make use of such infrastructure as it integrates with popular deep learning frameworks and supports acceleration of deep learning jobs using GPUs. In this talk, we share learnings and experiences in supporting different types of workloads in shared clusters equipped for doing deep learning as well as data engineering. We will cover the following topics: * Considerations for sharing the infrastructure for big data and deep learning in Spark * Deep learning in Spark in clusters with and without GPUs * Differences between distributed data processing and distributed machine learning * Multitenancy and isolation in shared infrastructure
Sa introduction to big data pipelining with cassandra & spark west mins...
This document provides an overview and outline of a 1-hour introduction to building a big data pipeline using Docker, Cassandra, Spark, Spark-Notebook and Akka. The introduction is presented as a half-day workshop at Devoxx November 2015. It uses a data pipeline environment from Data Fellas and demonstrates how to use scalable distributed technologies like Docker, Spark, Spark-Notebook and Cassandra to build a reactive, repeatable big data pipeline. The key takeaway is understanding how to construct such a pipeline.
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
This document describes BBVA's implementation of a Big Data Lake using Apache Spark for log collection, storage, and analytics. It discusses:
1) Using Syslog-ng for log collection from over 2,000 applications and devices, distributing logs to Kafka.
2) Storing normalized logs in HDFS and performing analytics using Spark, with outputs to analytics, compliance, and indexing systems.
3) Choosing Spark because it allows interactive, batch, and stream processing with one system using RDDs, SQL, streaming, and machine learning.
Introduction to Big Data Analytics using Apache Spark and Zeppelin on HDInsig...
Introduction to Big Data Analytics using Apache Spark on HDInsights on Azure (SaaS) and/or HDP on Azure(PaaS)
This workshop will provide an introduction to Big Data Analytics using Apache Spark using the HDInsights on Azure (SaaS) and/or HDP deployment on Azure(PaaS) . There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
Real time ETL processing using Spark streaming
The document discusses the architecture for real-time ETL processing, including using GoldenGate for change data capture from source databases, Kafka as the messaging system, and Spark jobs for streaming reconciliation and joining of data. It also covers requirements for the reconciler component like supporting idempotency, immutability, and schema evolution. Challenges with handling out-of-order events in Spark streaming and the data model used to address issues like idempotency and schema evolution are also described.
Spark as a Platform to Support Multi-Tenancy and Many Kinds of Data Applicati...
This document summarizes Uber's use of Spark as a data platform to support multi-tenancy and various data applications. Key points include:
- Uber uses Spark on YARN for resource management and isolation between teams/jobs. Parquet is used as the columnar file format for performance and schema support.
- Challenges include sharing infrastructure between many teams with different backgrounds and use cases. Spark provides a common platform.
- An Uber Development Kit (UDK) is used to help users get Spark jobs running quickly on Uber's infrastructure, with templates, defaults, and APIs for common tasks.
Big Telco - Yousun Jeong
This document provides an overview of SK Telecom's use of big data analytics and Spark. Some key points:
- SKT collects around 250 TB of data per day which is stored and analyzed using a Hadoop cluster of over 1400 nodes.
- Spark is used for both batch and real-time processing due to its performance benefits over other frameworks. Two main use cases are described: real-time network analytics and a network enterprise data warehouse (DW) built on Spark SQL.
- The network DW consolidates data from over 130 legacy databases to enable thorough analysis of the entire network. Spark SQL, dynamic resource allocation in YARN, and integration with BI tools help meet requirements for timely processing and quick
Data Science with Spark & Zeppelin
This document discusses Apache Zeppelin, an open-source web-based notebook that allows for interactive data analytics. It can be used for data exploration, visualization, collaboration and publishing. Zeppelin has deep integration with Apache Spark and supports multiple languages including Scala, Python, and SQL. It provides a Spark interpreter that allows users to analyze data using Spark without having to configure Spark themselves. The document demonstrates Zeppelin's functionality through examples and encourages readers to try it out and get involved in the community.
Continuous Processing in Structured Streaming with Jose Torres
This talk will cover the details of Continuous Processing in Structured Streaming and my work implementing the initial version in Spark 2.3 as well as the updates for 2.4. DStreams was Spark’s first attempt at streaming, and through dstream Spark became the first framework to provide both batch and streaming functionalities in one unified execution engine.
The way streaming execution happens is through this “micro-batch” model, in which the underlying execution engine simply runs on batches of data over and over again. Dstream’s design tightly couples the user-facing APIs with the execution model, and as a result was very difficult to accomplish certain tasks important in streaming, e.g. using event time and working with late data, without breaking the user-facing APIs. Structured Streaming was the 2nd (and the latest) major streaming effort in Spark. Its design decouples the frontend (user-facing APIs) and backend (execution), and allows us to change the execution model without any user API change.
However, the (historical) minimum possible latency for any record for DStreams or Structured Streaming was bounded by the amount of time that it takes to launch a task. This limitation is a result of the fact that the engine requires us to know both the starting and the ending offset, before any tasks are launched. In the worst case, the end-to-end latency is actually closer to the average batch time + task launching time. Continuous Processing removes this constraints and allows users to achieve sub-millisecond end-to-end latencies with the new execution engine.
This talk will take a technical deep dive into its capabilities, what it took to implement, and discuss the future developments.
Designing the Next Generation of Data Pipelines at Zillow with Apache Spark
The trade-off between development speed and pipeline maintainability is a constant for data engineers, especially for those in a rapidly evolving organization
Databricks Delta Lake and Its Benefits
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Spark Advanced Analytics NJ Data Science Meetup - Princeton University
Workshop - How to Build Recommendation Engine using Spark 1.6 and HDP
Hands-on - Build a Data analytics application using SPARK, Hortonworks, and Zeppelin. The session explains RDD concepts, DataFrames, sqlContext, use SparkSQL for working with DataFrames and explore graphical abilities of Zeppelin.
b) Follow along - Build a Recommendation Engine - This will show how to build a predictive analytics (MLlib) recommendation engine with scoring This will give a better understanding of architecture and coding in Spark for ML.
Taboola Road To Scale With Apache Spark
Taboola's data processing architecture has evolved over time from directly writing to databases to using Apache Spark for scalable real-time processing. Spark allows Taboola to process terabytes of data daily across multiple data centers for real-time recommendations, analytics, and algorithm calibration. Key aspects of Taboola's architecture include using Cassandra for event storage, Spark for distributed computing, Mesos for cluster management, and Zookeeper for coordination across a large Spark cluster.
Family data sheet HP Virtual Connect(May 2013)
This document provides an overview of HP Virtual Connect technology which simplifies network infrastructure by virtualizing server-to-network connections. Key features include consolidating network connections onto fewer modules to reduce costs, enabling bandwidth allocation per server as needed, and providing a centralized management console for multiple server enclosures. HP Virtual Connect offers various modules that provide Ethernet, Fibre Channel, and converged connectivity to simplify management and improve flexibility of the network environment.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure.
GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Stream All Things—Patterns of Modern Data Integration with Gwen Shapira
This document discusses patterns for modern data integration using streaming data. It outlines an evolution from data warehouses to data lakes to streaming data. It then describes four key patterns: 1) Stream all things (data) in one place, 2) Keep schemas compatible and process data on, 3) Enable ridiculously parallel single message transformations, and 4) Perform streaming data enrichment to add additional context to events. Examples are provided of using Apache Kafka and Kafka Connect to implement these patterns for a large hotel chain integrating various data sources and performing real-time analytics on customer events.
Tangram: Distributed Scheduling Framework for Apache Spark at Facebook
Tangram is a state-of-art resource allocator and distributed scheduling framework for Spark at Facebook with hierarchical queues and a resource based container abstraction. We support scheduling and resource management for a significant portion of Facebook's data warehouse and machine learning workloads that equates to running millions of jobs across several clusters with tens of thousands of machines. In this talk, we will describe Tangram's architecture, discuss Facebook's need for a custom scheduler, and explain how Tangram schedules Spark workloads at scale. We will specifically focus on several important features around improving Spark's efficiency, usability and reliability: 1. IO-rebalancer (Tetris) Support 2. User-Fairness Queueing 3. Heuristic-Based Backfill Scheduling Optimizations.
ASPgems - kappa architecture
Kappa Architecture is an alternative to Lambda Architecture that simplifies real-time data processing. It uses a distributed log like Kafka to store all input data immutably to allow reprocessing from the beginning if the processing code changes. This avoids having to maintain separate batch and real-time processing systems. The ASPgems team has implemented Kappa Architecture for several clients using Kafka, Spark Streaming, and Cassandra to provide real-time analytics and metrics in sectors like telecommunications, IoT, insurance, and energy.
Tableau
Data visualization is the process of visually representing information to help understand it more quickly. It is important because visuals allow humans to understand complex information instantly. Data visualization helps businesses make better decisions faster by communicating more information than tables and requiring less memory. Tableau is a popular business intelligence tool that allows users to interactively visualize and analyze data through drag-and-drop functionality. It can connect to various data sources and produce many chart types to provide rapid, real-time analysis of large datasets.
Spring Integration Splunk
Spring Integration allows for integration between applications using widely adopted integration patterns. The Splunk adaptors allow Spring Integration to ingest and export data from Splunk using its REST API and Java SDK. The inbound adaptor can search and export data from Splunk to message channels for filtering, transformation and exporting to other systems. The outbound adaptor can take data from other sources and push it to Splunk for indexing, searching and visualization.
More Related Content
What's hot
Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, L...
Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, L...TUMRA | Big Data Science - Gain a competitive advantage through Big Data & Data Science
This document provides a summary of using Apache Spark for continuous analytics and optimization. It discusses using Spark for collecting data from various sources, processing the data using Spark's capabilities for streaming, machine learning and SQL queries, and reporting insights. An example use case is presented for social media analysis using Spark Streaming to process a real-time data stream from Kafka and analyze the data using both the Spark SQL and core Spark APIs in Scala.Mining public datasets using opensource tools: Zeppelin, Spark and Juju
There are plenty of public datasets out there available and the number is growing. Few recent and most useful of BigData ecosystem tools are showcased: Apache Zeppelin (incubating), Apache Spark and Juju.
Infrastructure for Deep Learning in Apache Spark
In machine learning projects, the preparation of large datasets is a key phase which can be complex and expensive. It was traditionally done by data engineers before the handover to data scientists or ML engineers. They operated in different environments due to the differences in the tools, frameworks and runtimes required in each phase. Spark's support for different types of workloads brought data engineering closer to the downstream activities like machine learning that depended on the data. Unifying data acquisition, preprocessing, training models and batch inferencing under a single platform enabled by Spark not only provided seamless experience between different phases and helped accelerate the end-to-end ML lifecycle but also lowered the TCO in the building, managing the infrastructure to cover different phases. With that, the needs of a shared infrastructure expanded to include specialized hardware like GPUs and support deep learning workloads as well. Spark can effectively make use of such infrastructure as it integrates with popular deep learning frameworks and supports acceleration of deep learning jobs using GPUs. In this talk, we share learnings and experiences in supporting different types of workloads in shared clusters equipped for doing deep learning as well as data engineering. We will cover the following topics: * Considerations for sharing the infrastructure for big data and deep learning in Spark * Deep learning in Spark in clusters with and without GPUs * Differences between distributed data processing and distributed machine learning * Multitenancy and isolation in shared infrastructure
Sa introduction to big data pipelining with cassandra & spark west mins...
This document provides an overview and outline of a 1-hour introduction to building a big data pipeline using Docker, Cassandra, Spark, Spark-Notebook and Akka. The introduction is presented as a half-day workshop at Devoxx November 2015. It uses a data pipeline environment from Data Fellas and demonstrates how to use scalable distributed technologies like Docker, Spark, Spark-Notebook and Cassandra to build a reactive, repeatable big data pipeline. The key takeaway is understanding how to construct such a pipeline.
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
This document describes BBVA's implementation of a Big Data Lake using Apache Spark for log collection, storage, and analytics. It discusses:
1) Using Syslog-ng for log collection from over 2,000 applications and devices, distributing logs to Kafka.
2) Storing normalized logs in HDFS and performing analytics using Spark, with outputs to analytics, compliance, and indexing systems.
3) Choosing Spark because it allows interactive, batch, and stream processing with one system using RDDs, SQL, streaming, and machine learning.
Introduction to Big Data Analytics using Apache Spark and Zeppelin on HDInsig...
Introduction to Big Data Analytics using Apache Spark on HDInsights on Azure (SaaS) and/or HDP on Azure(PaaS)
This workshop will provide an introduction to Big Data Analytics using Apache Spark using the HDInsights on Azure (SaaS) and/or HDP deployment on Azure(PaaS) . There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
Real time ETL processing using Spark streaming
The document discusses the architecture for real-time ETL processing, including using GoldenGate for change data capture from source databases, Kafka as the messaging system, and Spark jobs for streaming reconciliation and joining of data. It also covers requirements for the reconciler component like supporting idempotency, immutability, and schema evolution. Challenges with handling out-of-order events in Spark streaming and the data model used to address issues like idempotency and schema evolution are also described.
Spark as a Platform to Support Multi-Tenancy and Many Kinds of Data Applicati...
This document summarizes Uber's use of Spark as a data platform to support multi-tenancy and various data applications. Key points include:
- Uber uses Spark on YARN for resource management and isolation between teams/jobs. Parquet is used as the columnar file format for performance and schema support.
- Challenges include sharing infrastructure between many teams with different backgrounds and use cases. Spark provides a common platform.
- An Uber Development Kit (UDK) is used to help users get Spark jobs running quickly on Uber's infrastructure, with templates, defaults, and APIs for common tasks.
Big Telco - Yousun Jeong
This document provides an overview of SK Telecom's use of big data analytics and Spark. Some key points:
- SKT collects around 250 TB of data per day which is stored and analyzed using a Hadoop cluster of over 1400 nodes.
- Spark is used for both batch and real-time processing due to its performance benefits over other frameworks. Two main use cases are described: real-time network analytics and a network enterprise data warehouse (DW) built on Spark SQL.
- The network DW consolidates data from over 130 legacy databases to enable thorough analysis of the entire network. Spark SQL, dynamic resource allocation in YARN, and integration with BI tools help meet requirements for timely processing and quick
Data Science with Spark & Zeppelin
This document discusses Apache Zeppelin, an open-source web-based notebook that allows for interactive data analytics. It can be used for data exploration, visualization, collaboration and publishing. Zeppelin has deep integration with Apache Spark and supports multiple languages including Scala, Python, and SQL. It provides a Spark interpreter that allows users to analyze data using Spark without having to configure Spark themselves. The document demonstrates Zeppelin's functionality through examples and encourages readers to try it out and get involved in the community.
Continuous Processing in Structured Streaming with Jose Torres
This talk will cover the details of Continuous Processing in Structured Streaming and my work implementing the initial version in Spark 2.3 as well as the updates for 2.4. DStreams was Spark’s first attempt at streaming, and through dstream Spark became the first framework to provide both batch and streaming functionalities in one unified execution engine.
The way streaming execution happens is through this “micro-batch” model, in which the underlying execution engine simply runs on batches of data over and over again. Dstream’s design tightly couples the user-facing APIs with the execution model, and as a result was very difficult to accomplish certain tasks important in streaming, e.g. using event time and working with late data, without breaking the user-facing APIs. Structured Streaming was the 2nd (and the latest) major streaming effort in Spark. Its design decouples the frontend (user-facing APIs) and backend (execution), and allows us to change the execution model without any user API change.
However, the (historical) minimum possible latency for any record for DStreams or Structured Streaming was bounded by the amount of time that it takes to launch a task. This limitation is a result of the fact that the engine requires us to know both the starting and the ending offset, before any tasks are launched. In the worst case, the end-to-end latency is actually closer to the average batch time + task launching time. Continuous Processing removes this constraints and allows users to achieve sub-millisecond end-to-end latencies with the new execution engine.
This talk will take a technical deep dive into its capabilities, what it took to implement, and discuss the future developments.
Designing the Next Generation of Data Pipelines at Zillow with Apache Spark
The trade-off between development speed and pipeline maintainability is a constant for data engineers, especially for those in a rapidly evolving organization
Databricks Delta Lake and Its Benefits
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Spark Advanced Analytics NJ Data Science Meetup - Princeton University
Workshop - How to Build Recommendation Engine using Spark 1.6 and HDP
Hands-on - Build a Data analytics application using SPARK, Hortonworks, and Zeppelin. The session explains RDD concepts, DataFrames, sqlContext, use SparkSQL for working with DataFrames and explore graphical abilities of Zeppelin.
b) Follow along - Build a Recommendation Engine - This will show how to build a predictive analytics (MLlib) recommendation engine with scoring This will give a better understanding of architecture and coding in Spark for ML.
Taboola Road To Scale With Apache Spark
Taboola's data processing architecture has evolved over time from directly writing to databases to using Apache Spark for scalable real-time processing. Spark allows Taboola to process terabytes of data daily across multiple data centers for real-time recommendations, analytics, and algorithm calibration. Key aspects of Taboola's architecture include using Cassandra for event storage, Spark for distributed computing, Mesos for cluster management, and Zookeeper for coordination across a large Spark cluster.
Family data sheet HP Virtual Connect(May 2013)
This document provides an overview of HP Virtual Connect technology which simplifies network infrastructure by virtualizing server-to-network connections. Key features include consolidating network connections onto fewer modules to reduce costs, enabling bandwidth allocation per server as needed, and providing a centralized management console for multiple server enclosures. HP Virtual Connect offers various modules that provide Ethernet, Fibre Channel, and converged connectivity to simplify management and improve flexibility of the network environment.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure.
GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Stream All Things—Patterns of Modern Data Integration with Gwen Shapira
This document discusses patterns for modern data integration using streaming data. It outlines an evolution from data warehouses to data lakes to streaming data. It then describes four key patterns: 1) Stream all things (data) in one place, 2) Keep schemas compatible and process data on, 3) Enable ridiculously parallel single message transformations, and 4) Perform streaming data enrichment to add additional context to events. Examples are provided of using Apache Kafka and Kafka Connect to implement these patterns for a large hotel chain integrating various data sources and performing real-time analytics on customer events.
Tangram: Distributed Scheduling Framework for Apache Spark at Facebook
Tangram is a state-of-art resource allocator and distributed scheduling framework for Spark at Facebook with hierarchical queues and a resource based container abstraction. We support scheduling and resource management for a significant portion of Facebook's data warehouse and machine learning workloads that equates to running millions of jobs across several clusters with tens of thousands of machines. In this talk, we will describe Tangram's architecture, discuss Facebook's need for a custom scheduler, and explain how Tangram schedules Spark workloads at scale. We will specifically focus on several important features around improving Spark's efficiency, usability and reliability: 1. IO-rebalancer (Tetris) Support 2. User-Fairness Queueing 3. Heuristic-Based Backfill Scheduling Optimizations.
ASPgems - kappa architecture
Kappa Architecture is an alternative to Lambda Architecture that simplifies real-time data processing. It uses a distributed log like Kafka to store all input data immutably to allow reprocessing from the beginning if the processing code changes. This avoids having to maintain separate batch and real-time processing systems. The ASPgems team has implemented Kappa Architecture for several clients using Kafka, Spark Streaming, and Cassandra to provide real-time analytics and metrics in sectors like telecommunications, IoT, insurance, and energy.
What's hot (20)
Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, L...
Continuous Analytics & Optimisation using Apache Spark (Big Data Analytics, L...
Mining public datasets using opensource tools: Zeppelin, Spark and Juju
Mining public datasets using opensource tools: Zeppelin, Spark and Juju
Sa introduction to big data pipelining with cassandra & spark west mins...
Sa introduction to big data pipelining with cassandra & spark west mins...
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
Introduction to Big Data Analytics using Apache Spark and Zeppelin on HDInsig...
Introduction to Big Data Analytics using Apache Spark and Zeppelin on HDInsig...
Spark as a Platform to Support Multi-Tenancy and Many Kinds of Data Applicati...
Spark as a Platform to Support Multi-Tenancy and Many Kinds of Data Applicati...
Continuous Processing in Structured Streaming with Jose Torres
Continuous Processing in Structured Streaming with Jose Torres
Designing the Next Generation of Data Pipelines at Zillow with Apache Spark
Designing the Next Generation of Data Pipelines at Zillow with Apache Spark
Spark Advanced Analytics NJ Data Science Meetup - Princeton University
Spark Advanced Analytics NJ Data Science Meetup - Princeton University
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Stream All Things—Patterns of Modern Data Integration with Gwen Shapira
Stream All Things—Patterns of Modern Data Integration with Gwen Shapira
Tangram: Distributed Scheduling Framework for Apache Spark at Facebook
Tangram: Distributed Scheduling Framework for Apache Spark at Facebook
Similar to Future of data visualization
Tableau
Data visualization is the process of visually representing information to help understand it more quickly. It is important because visuals allow humans to understand complex information instantly. Data visualization helps businesses make better decisions faster by communicating more information than tables and requiring less memory. Tableau is a popular business intelligence tool that allows users to interactively visualize and analyze data through drag-and-drop functionality. It can connect to various data sources and produce many chart types to provide rapid, real-time analysis of large datasets.
Spring Integration Splunk
Spring Integration allows for integration between applications using widely adopted integration patterns. The Splunk adaptors allow Spring Integration to ingest and export data from Splunk using its REST API and Java SDK. The inbound adaptor can search and export data from Splunk to message channels for filtering, transformation and exporting to other systems. The outbound adaptor can take data from other sources and push it to Splunk for indexing, searching and visualization.
Big data analytics with hadoop volume 2
This document discusses big data analytics using Hadoop. It provides an overview of loading clickstream data from websites into Hadoop using Flume and refining the data with MapReduce. It also describes how Hive and HCatalog can be used to query and manage the data, presenting it in a SQL-like interface. Key components and processes discussed include loading data into a sandbox, Flume's architecture and data flow, using MapReduce for parallel processing, how HCatalog exposes Hive metadata, and how Hive allows querying data using SQL queries.
IRMAC April 2015 - DMBOK2 DWBI New Content
This document outlines the agenda for a presentation on data management and business intelligence. It includes introductions, a history of reporting and data warehousing approaches, discussions of BI tools, the Data Management Body of Knowledge (DMBOK), data vault modeling, Hadoop, data visualization, data sciences, virtualization, and the DMBOK conceptual architecture. Case studies and examples are provided to illustrate key concepts in data management and analytics.
Jason Huang, Solutions Engineer, Qubole at MLconf ATL - 9/18/15
Sparking Data in the Cloud: Data isn’t useful until it’s used to drive decision-making. Companies, like Pinterest, are using Machine Learning to build data-driven recommendation engines and perform advanced cluster analysis. In this talk, Praveen Seluka will cover best practices for running Spark in the cloud, common challenges in iterative design and interactive analysis.
Atlanta MLConf
Qubole presentation by Jason Huang, Sr. Solutions Architect for Qubole at the MLConf in Atlanta September 18th, 2015
SplunkLive! Amsterdam 2015 - Web Framework & 3rd Party Visualization
Besides seeing the newest features in Splunk Enterprise, we will show you how to use the Splunk Web Framework and 3rd party visualisations to create rich, interactive experiences using Splunk and its analytical capabilities.
SEMLIB Final Conference | DERI presentation
The document summarizes a recommendation engine that leverages linked open data. It describes (1) using SPARQL to input linked data and output recommendations as linked data, (2) implementing collaborative filtering and content-based recommendation algorithms adapted for linked data, and (3) building a distributed and parallel framework using MapReduce to handle large linked data inputs and ensure scalability.
Transitioning Compute Models: Hadoop MapReduce to Spark
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
1) StumbleUpon uses open source tools like Kafka, HBase, Hive and Pig to build a scalable big data infrastructure to process large amounts of data from its services in real-time and batch.
2) Data is collected from various services using Kafka and stored in HBase for real-time analytics. Batch processing is done using Pig and data is loaded into Hive for ad-hoc querying.
3) The infrastructure powers various applications like recommendations, ads and business intelligence dashboards.
Resume_VipinKP
This document contains the resume of Vipin KP, who has over 5 years of experience as a Big Data Hadoop Developer. He has extensive experience developing Hadoop applications for clients such as EMC, Apple, Dun & Bradstreet, Neilsen, Commonwealth Bank of Australia, and Nokia Siemens Network. He has expertise in technologies such as Hadoop, Hive, Pig, Sqoop, Oozie, and Spark and has developed ETL processes, data pipelines, and analytics solutions on Hadoop clusters. He holds a Master's degree in Computer Science and is Cloudera certified in Hadoop development.
Advanced Visualization of Spark jobs
This document discusses an advanced visualization tool for Spark and Flink jobs. It collects fine-grained data about task execution, including data characteristics and block fetch information. This information is exposed through a REST API and used to visualize the physical execution plan, detect issues like data skew, and help developers optimize their applications. The tool aims to help understand distributed data processing systems and guide testing of adaptive partitioning techniques. It has been extended to support Flink visualization as well. Future plans include open-sourcing the framework and adding more visualization features and metrics.
Sparkflows.io
Sparkflows provides a solution to reduce the cost and time required to develop big data analytics applications from months to hours. It offers a visual workflow editor that allows data analysts, data scientists, and data engineers to easily build analytics workflows by dragging and dropping nodes without extensive coding. Some key benefits include interactive execution, rich visualizations, pre-built workflows for common use cases, and the ability to deploy complex pipelines in minutes.
SplunkLive London 2014 Developer Presentation
This document provides an overview of Splunk's developer platform. It introduces Jon Rooney, Director of Developer Marketing at Splunk, and Damien Dallimore, Developer Evangelist. It discusses how Splunk can help with application development challenges like visibility across the development lifecycle. It also demonstrates how Splunk can integrate with the development process using tools like its REST API and SDKs. The document highlights Splunk's modular inputs, web framework, and opportunities for custom visualizations and search commands. Overall, it aims to showcase Splunk's powerful platform for developers.
Pallavi_Resume
The document provides a summary of Pallavi's professional experience and skills. She has over 8 years of experience working with big data, databases, and web applications. Some of her key skills and experiences include developing ETL processes using tools like Apache Spark, Hive, Pig, Sqoop and Flume; loading and analyzing data in Hadoop clusters; creating dashboards and reports in Tableau; and developing applications using technologies like SQL Server, SSIS, SSRS, Java, and .NET. She has worked on projects involving healthcare, performance metrics, and business intelligence.
Big Data at Oracle - Strata 2015 San Jose
Strata 2015 presentation from Oracle for Big Data - we are announcing several new big data products including GoldenGate for Big Data, Big Data Discovery, Oracle Big Data SQL and Oracle NoSQL
Ben Gardner | Delivering a Linked Data warehouse and integrating across the w...
- The document discusses building a linked data warehouse at a law firm to integrate siloed information from diverse sources and allow for flexible information discovery.
- They extracted data from various sources like Excel reports, XML files, and databases into RDF triples and loaded them into a triple store with an ETL platform.
- This created a logical data warehouse that connects matters, people, clients, and other entities as "things" with relationships, allowing exploration from different domain views.
- An OData integration allows querying the triple store from tools like Excel and Tableau, while a web interface demonstrates lens-based exploration of entities and composing advanced semantic searches.
DeepeshRehi
Deepesh Rehi has over 5 years of experience in big data technologies like Hadoop, Spark, Hive, and Neo4j. He has worked on several projects involving fraud detection, data integration, and analytics. Currently, he is working as a Programmer Analyst at AAA Insurance where he is building a fraud detection system using graph databases and Spark. Previously, he developed applications for data ingestion, ETL, and analytics on projects for clients like Capital One and Cognizant. Deepesh has strong skills in Scala, Java, SQL, and Unix scripting.
Best-Practices-for-Using-Tableau-With-Snowflake.pdf
This whitepaper discusses best practices for using Tableau with Snowflake. Snowflake is a cloud-based data warehouse as a service that provides limitless scalability and no hardware or software to install and maintain. Tableau is a business intelligence software that allows users to visualize, explore and analyze data. The whitepaper provides an overview of Snowflake and Tableau and discusses topics such as connecting Tableau to Snowflake, working with different data types and structures in Snowflake, implementing security, and optimizing performance.
Apache Spark: Lightning Fast Cluster Computing
Apache Spark is an open-source cluster computing framework for fast and large-scale data processing. It uses an in-memory data abstraction called resilient distributed datasets (RDDs) that allow parallel operations on large datasets across a cluster. Spark also provides APIs in Java, Scala, Python and R for interactive data analysis through its core engine as well as high-level libraries for SQL, streaming, machine learning and graph processing.
Similar to Future of data visualization (20)
Jason Huang, Solutions Engineer, Qubole at MLconf ATL - 9/18/15
Jason Huang, Solutions Engineer, Qubole at MLconf ATL - 9/18/15
SplunkLive! Amsterdam 2015 - Web Framework & 3rd Party Visualization
SplunkLive! Amsterdam 2015 - Web Framework & 3rd Party Visualization
Transitioning Compute Models: Hadoop MapReduce to Spark
Transitioning Compute Models: Hadoop MapReduce to Spark
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...
Ben Gardner | Delivering a Linked Data warehouse and integrating across the w...
Ben Gardner | Delivering a Linked Data warehouse and integrating across the w...
Best-Practices-for-Using-Tableau-With-Snowflake.pdf
Best-Practices-for-Using-Tableau-With-Snowflake.pdf
Recently uploaded
National Security Agency - NSA mobile device best practices
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.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
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
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
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.
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
TrustArc Webinar - 2024 Global Privacy Survey
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
Communications Mining Series - Zero to Hero - Session 1
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
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
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.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Recently uploaded (20)
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Future of data visualization
- 1. hadoopsphere Future of DataVisualization HadoopSphereVirtual Conclave August 2015
- 2. 2 Commonly understood components of data visualization • Graphs, maps, tables, shapes • WYSIWYG editors • Dashboards • HTML5 views • Infographics
- 3. 3 Defining data visualization • Data visualization is the presentation of data in a pictorial or graphical format. -Wikipedia • Data visualization is a visual representation of the insights gained from your analysis. - Datameer
- 4. 4 EmergingTrends • New Channels – Mobile,VR devices • More interactive charts – Redraw, filter, annotations • Multidimensional visual – VR, GL • Network visualization – Social, Linkages • Collaborations – Share, Review,Workflow • And we may have ‘audiolizations’ as well – Audio narrations
- 5. 5 Process of data visualization Prepare Explore Design Deliver
- 6. 6 Challenges Access to data Parse data Central data access Fast queries Complex visual types Linked Views Data mining Collaboration Workflow
- 7. 7 Introducing Apache Zeppelin HDFS/ Data Store Operations Governance/Security YARN Spark / Flink /Tajo … • Apache Zeppelin is a web-based multi-purpose notebook for interactive data analysis. • It is a 100% open source incubator project of Apache Software Foundations. • As per HadoopSphere,Apache Zeppelin is going to influence big data visualization tools for next 2 years or more.
- 8. 8 Zeppelin Notebook • A web-based notebook that enables interactive data analytics. • You can type in code in SQL, Scala and more in the notebook. • Run the commands directly from the notebook. Source for this slide and subsequent slides: (1) http://zeppelin.apache.org (2) Lee Moon Soo, Introduction to Zeppelin, ApacheCon 2015
- 10. 10 Behind the scenes • Java based backend • Active development community - Built-in Apache Spark integration - Uses Angular JS, D3.js - Tested on Mac OSx, Ubuntu 14.x, CentOS 6.x
- 11. 11 Zeppelin features -Visualization • Some basic charts are currently included in Zeppelin and more will be added in future. • Visualizations are not limited to Spark SQL's query - relational output from many other language backends can be recognized and visualized.
- 12. 12 Zeppelin features - Pivots • With simple drag and drop Zeppelin aggregates the values and display them in pivot chart. • You can easily create chart with multiple aggregated values including sum, count, average, min, max.
- 13. 13 Zeppelin features – Dynamic forms • Zeppelin can dynamically take inputs in forms as part of the notebook. • These dynamic forms can be used to see input based results or render charts.
- 14. 14 Zeppelin features – Collaboration and publishing • Notebook URL can be shared among collaborators. Zeppelin can then broadcast any changes in real time, just like the collaboration in Google docs. • Zeppelin provides a URL to display the results only that can easily be embedded as an iframe inside a web page.
- 15. 15 Zeppelin interpreter architecture • Zeppelin Interpreter is a connector between Zeppelin and backend data processing system. For example to use scala code in Zeppelin, you need scala interpreter. • Every Interpreter belongs to an InterpreterGroup which is a unit of start/stop interpreter. Interpreters in the same InterpreterGroup can reference each other. For example, SparkSqlInterpreter can reference SparkInterpreter to get SparkContext from it while they're in the same group. ZeppelinServer InterpreterGroup Separate JVM process Interpreter Interpreter Interpreter Spark Spark PySpark SparkSQL Dep Load libraries Maven repositorySpark cluster Share single SparkDriver Thrift
- 16. 16 Zeppelin interaction ecosystem * includes future roadmap components
- 17. 17 Getting involved with Zeppelin • http://zeppelin.apache.org/ • http://github.com/apache/incubator-zeppelin Installation reference: • http://hortonworks.com/blog/introduction-to-data-science- with-apache-spark/ • http://nflabs.github.io/z-manager/ Mailing List • users@zeppelin.incubator.apache.org
- 18. 18 Other Notebook options • iPython Notebook • Beaker • Spark-Notebook • Databricks Cloud Notebook