Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms
Implementing a highly scalable stock prediction system with R, Geode, SpringX...William Markito Oliveira
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms.
A Stock Prediction System using Open-Source SoftwareFred Melo
Presentation delivered at Apache Con 2015 conference. Talks about the architecture and implementation of a real-time prediction system based on live streaming for stock quotes using Spring XD, .
A demo was also featured.
Optimizing your SparkML pipelines using the latest features in Spark 2.3DataWorks Summit
The document discusses optimizing Spark machine learning pipelines. It describes using parallel model evaluation to speed up hyperparameter tuning by training multiple models simultaneously. This reduces the time spent on cross-validation for hyperparameter selection. The document also discusses optimizing tuning for pipeline models by treating the pipeline as a directed acyclic graph and parallelizing the fitting in breadth-first order to avoid duplicating work where possible.
Risk Management Framework Using Intel FPGA, Apache Spark, and Persistent RDDs...Databricks
Performing analytics for risk management purposes is applied in many fields, especially in financial services. We present a framework for accelerated risk analytics and show a large-scale financial sector application where this framework is used to run backtesting algorithms on risk-based securities such as options. These applications require highly computationally-intensive operations on extremely large data sets with objects numbering in the tens of billions.
Intel FPGA and FinLib library for financial applications are used to offload the computation; however, another challenging problem (that we have resolved) is how to feed the data to the FPGA at the optimal speed without having to do customized coding. A combination of Apache Spark along with Levyx’s persistent dataframes are used to address this problem. These dataframes allow absorbing the computation from Spark and offloading it to Finlib in an automated way. This example can be expanded to many other areas of Risk Management such as Insurance and Cybersecurity.
Spark SQL is a module for structured data processing on Spark. It integrates relational processing with Spark's functional programming API and allows SQL queries to be executed over data sources via the Spark execution engine. Spark SQL includes components like a SQL parser, a Catalyst optimizer, and Spark execution engines for queries. It supports HiveQL queries, SQL queries, and APIs in Scala, Java, and Python.
Sparkta is an open source real-time analytics platform based on Apache Spark. It allows users to define aggregation policies in JSON documents without coding, and processes streaming data in real-time. The platform utilizes technologies like Apache Kite, Spark Streaming, and Kafka to ingest data from various sources and store aggregated outputs. Stratio is developing Sparkta to be a fully-featured, distributed, high-volume, and pluggable analytics framework.
Improving Python and Spark (PySpark) Performance and InteroperabilityWes McKinney
Slides from Spark Summit East 2017 — February 9, 2017 in Boston. Discusses ongoing development work to accelerate Python-on-Spark performance using Apache Arrow and other tools
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms
Implementing a highly scalable stock prediction system with R, Geode, SpringX...William Markito Oliveira
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms.
A Stock Prediction System using Open-Source SoftwareFred Melo
Presentation delivered at Apache Con 2015 conference. Talks about the architecture and implementation of a real-time prediction system based on live streaming for stock quotes using Spring XD, .
A demo was also featured.
Optimizing your SparkML pipelines using the latest features in Spark 2.3DataWorks Summit
The document discusses optimizing Spark machine learning pipelines. It describes using parallel model evaluation to speed up hyperparameter tuning by training multiple models simultaneously. This reduces the time spent on cross-validation for hyperparameter selection. The document also discusses optimizing tuning for pipeline models by treating the pipeline as a directed acyclic graph and parallelizing the fitting in breadth-first order to avoid duplicating work where possible.
Risk Management Framework Using Intel FPGA, Apache Spark, and Persistent RDDs...Databricks
Performing analytics for risk management purposes is applied in many fields, especially in financial services. We present a framework for accelerated risk analytics and show a large-scale financial sector application where this framework is used to run backtesting algorithms on risk-based securities such as options. These applications require highly computationally-intensive operations on extremely large data sets with objects numbering in the tens of billions.
Intel FPGA and FinLib library for financial applications are used to offload the computation; however, another challenging problem (that we have resolved) is how to feed the data to the FPGA at the optimal speed without having to do customized coding. A combination of Apache Spark along with Levyx’s persistent dataframes are used to address this problem. These dataframes allow absorbing the computation from Spark and offloading it to Finlib in an automated way. This example can be expanded to many other areas of Risk Management such as Insurance and Cybersecurity.
Spark SQL is a module for structured data processing on Spark. It integrates relational processing with Spark's functional programming API and allows SQL queries to be executed over data sources via the Spark execution engine. Spark SQL includes components like a SQL parser, a Catalyst optimizer, and Spark execution engines for queries. It supports HiveQL queries, SQL queries, and APIs in Scala, Java, and Python.
Sparkta is an open source real-time analytics platform based on Apache Spark. It allows users to define aggregation policies in JSON documents without coding, and processes streaming data in real-time. The platform utilizes technologies like Apache Kite, Spark Streaming, and Kafka to ingest data from various sources and store aggregated outputs. Stratio is developing Sparkta to be a fully-featured, distributed, high-volume, and pluggable analytics framework.
Improving Python and Spark (PySpark) Performance and InteroperabilityWes McKinney
Slides from Spark Summit East 2017 — February 9, 2017 in Boston. Discusses ongoing development work to accelerate Python-on-Spark performance using Apache Arrow and other tools
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...Databricks
Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a “pure SaaS” company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems.
As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark.
In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism’s Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support interactive user experience.
Finally, we will share the future challenges for 2018 and beyond.
Pandas UDF: Scalable Analysis with Python and PySparkLi Jin
Over the past few years, Python has become the default language for data scientists. Packages such as pandas, numpy, statsmodel, and scikit-learn have gained great adoption and become the mainstream toolkits. At the same time, Apache Spark has become the de facto standard in processing big data. Spark ships with a Python interface, aka PySpark, however, because Spark’s runtime is implemented on top of JVM, using PySpark with native Python library sometimes results in poor performance and usability.
In this talk, we introduce a new type of PySpark UDF designed to solve this problem – Vectorized UDF. Vectorized UDF is built on top of Apache Arrow and bring you the best of both worlds – the ability to define easy to use, high performance UDFs and scale up your analysis with Spark.
Sherlock: an anomaly detection service on top of Druid DataWorks Summit
Sherlock is an anomaly detection service built on top of Druid. It leverages EGADS (Extensible Generic Anomaly Detection System; github.com/yahoo/egads) to detect anomalies in time-series data. Users can schedule jobs on an hourly, daily, weekly, or monthly basis, view anomaly reports from Sherlock's interface, or receive them via email.
Sherlock has four major components: timeseries generation, EGADS anomaly detection, Redis backend and Spark Java UI. Timeseries generation involves building, validating, querying, parsing the Druid query. Parsed Druid response is then fed to EGADS anomaly detection component which detects and generates the anomaly reports for each input time-series data. Sherlock uses Redis backend to store jobs metadata, generated anomaly reports and persistent job queue for scheduling jobs, etc. Users can choose to have a clustered Redis or standalone Redis. Sherlock provides user interface built with Spark Java. The UI enables users to submit instant anomaly analysis, create, and launch detection jobs, view anomalies on a heatmap and on a graph. Jigarkumar Patel, Software Development Engineer I, Oath Inc. and, David Servose, Software Systems Engineer, Oath
Secured (Kerberos-based) Spark Notebook for Data Science: Spark Summit East t...Spark Summit
The document discusses securing Spark notebooks for data science by integrating Kerberos authentication. It begins with an overview of Spark notebooks and the current authentication approach. It then covers the requirements for Kerberos integration, how Kerberos works in HDFS and Yarn clusters, and a proposed design to integrate Kerberos into JupyterHub, SparkMagic and Livy to authenticate users and allow secured access to HDFS and Spark from notebooks. Key aspects of the design include custom JupyterHub authenticators and spawners, obtaining service tickets from the KDC, and propagating user identities through the system.
Big Data Meets Learning Science: Keynote by Al EssaSpark Summit
How do we learn and how can we learn better? Educational technology is undergoing a revolution fueled by learning science and data science. The promise is to make a high-quality personalized education accessible and affordable by all. In this presentation Alfred will describe how Apache Spark and Databricks are at the center of the innovation pipeline at McGraw Hill for developing next-generation learner models and algorithms in support of millions of learners and instructors worldwide.
Uber has created a Data Science Workbench to improve the productivity of its data scientists by providing scalable tools, customization, and support. The Workbench provides Jupyter notebooks for interactive coding and visualization, RStudio for rapid prototyping, and Apache Spark for distributed processing. It aims to centralize infrastructure provisioning, leverage Uber's distributed backend, enable knowledge sharing and search, and integrate with Uber's data ecosystem tools. The Workbench manages Docker containers of tools like Jupyter and RStudio running on a Mesos cluster, with files stored in a shared file system. It addresses the problems of wasted time from separate infrastructures and lack of tool standardization across Uber's data science teams.
The challenge of computing big data for evolving digital business processes demands variety of computation techniques and engines (SQL, OLAP, time-series, graph, document store), but working in unified framework. A simple architecture of data transformations while ensuring the security, governance, and operational administration are the necessary critical components for enterprise production environments supporting day-to-day business processes. In this session, you will learn about best practices & critical components to ensure business value from latest production deployments. Hear how existing customers are using SAP Vora and the value they have achieved so far with this in-memory engine for distributed data processing. The session provides you with a clear understanding how SAP Vora and open source components like Apache Hadoop and Apache Spark offer an architecture that supports a wide variety of use cases and industries. You will also receive very useful insight where to find development resources, test drive demos, and general documentation.
Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...Spark Summit
In many cases, Big Data becomes just another buzzword because of the lack of tools that can support both the technological requirements for developing and deploying of the projects and/or the fluency of communication between the different profiles of people involved in the projects.
In this talk, we will present Moriarty, a set of tools for fast prototyping of Big Data applications that can be deployed in an Apache Spark environment. These tools support the creation of Big Data workflows using the already existing functional blocks or supporting the creation of new functional blocks. The created workflow can then be deployed in a Spark infrastructure and used through a REST API.
For better understanding of Moriarty, the prototyping process and the way it hides the Spark environment to the Big Data users and developers, we will present it together with a couple of examples based on a Industry 4.0 success cases and other on a logistic success case.
Data Con LA 2020
Description
Apache Druid is a cloud-native open-source database that enables developers to build highly-scalable, low-latency, real-time interactive dashboards and apps to explore huge quantities of data. This column-oriented database provides the microsecond query response times required for ad-hoc queries and programmatic analytics. Druid natively streams data from Apache Kafka (and more) and batch loads just about anything. At ingestion, Druid partitions data based on time so time-based queries run significantly faster than traditional databases, plus Druid offers SQL compatibility. Druid is used in production by AirBnB, Nielsen, Netflix and more for real-time and historical data analytics. This talk provides an introduction to Apache Druid including: Druid's core architecture and its advantages, Working with streaming and batch data in Druid, Querying data and building apps on Druid and Real-world examples of Apache Druid in action
Speaker
Matt Sarrel, Imply Data, Developer Evangelist
This document discusses the history and development of Python data analysis tools, including pandas. It covers Wes McKinney's work on pandas from 2008 to the present, including the motivations for making data analysis easier and more productive. It also summarizes the development of related projects like Apache Arrow for standardizing columnar data representations to improve code reuse across languages.
Using Apache Arrow, Calcite, and Parquet to Build a Relational CacheDremio Corporation
From DataEngConf 2017 - Everybody wants to get to data faster. As we move from more general solution to specific optimization techniques, the level of performance impact grows. This talk will discuss how layering in-memory caching, columnar storage and relational caching can combine to provide a substantial improvement in overall data science and analytical workloads. It will include a detailed overview of how you can use Apache Arrow, Calcite and Parquet to achieve multiple magnitudes improvement in performance over what is currently possible.
The Future of Column-Oriented Data Processing With Apache Arrow and Apache Pa...Dremio Corporation
Essentially every successful analytical DBMS in the market today makes use of column-oriented data structures. In the Hadoop ecosystem, Apache Parquet (and Apache ORC) provide similar advantages in terms of processing and storage efficiency. Apache Arrow is the in-memory counterpart to these formats and has been been embraced by over a dozen open source projects as the de facto standard for in-memory processing. In this session the PMC Chair for Apache Arrow and the PMC Chair for Apache Parquet discuss the future of column-oriented processing.
At ING we needed a way to implement Data science models from exploration into production. I will do this talk from my experience on the exploration and production Hadoop environment as a senior Ops engineer. For this we are using OpenShift to run Docker containers that connect to the big data Hadoop environment.
During this talk I will explain why we need this and how this is done at ING. Also how to set up a docker container running a data science model using Hive, Python, and Spark. I’ll explain how to use Docker files to build Docker images, add all the needed components inside the Docker image, and how to run different versions of software in different containers.
In the end I will also give a demo of how it runs and is automated using Git with webhook connecting to Jenkins and start the docker service that will connect to a big data Hadoop environment.
This is going to be a great technical talk for engineers and data scientist.
Speaker
Lennard Cornelis, Ops Engineer, ING
Adding structure to your streaming pipelines: moving from Spark streaming to ...DataWorks Summit
How do you go from a strictly typed object-based streaming pipeline with simple operations to a structured streaming pipeline with higher order complex relational operations? This is what the Data Engineering team did at GoPro to scale up the development of streaming pipelines for the rapidly growing number of devices and applications.
When big data frameworks such as Hadoop first came to exist, developers were happy because we could finally process large amounts of data without writing complex multi-threaded code or worse yet writing complicated distributed code. Unfortunately, only very simple operations were available such as map and reduce. Almost immediately, higher level operations were desired similar to relational operations. And so Hive and dozens (hundreds?) of SQL-based big data tools became available for more developer-efficient batch processing of massive amounts of data.
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world, so that nearly every streaming framework now supports higher level relational operations.
In this talk, we will discuss in a very hands-on manner how the streaming data pipelines for GoPro devices and apps have moved from the original Spark streaming with its simple RDD-based operations in Spark 1.x to Spark's structured streaming with its higher level relational operations in Spark 2.x. We will talk about the differences, advantages, and necessary pain points that must be addressed in order to scale relational-based streaming pipelines for massive IoT streams. We will also talk about moving from “hand built” Hadoop/Spark clusters running in the cloud to using a Spark-based cloud service. DAVID WINTERS, Big Data Architect, GoPro and HAO ZOU, Senior Software Engineer, GoPro
When Apache Spark Meets TiDB with Xiaoyu MaDatabricks
During the past 10 years, big-data storage layers mainly focus on analytical use cases. When it comes to analytical cases, users usually offload data onto Hadoop cluster and perform queries on HDFS files. People struggle dealing with modifications on append only storage and maintain fragile ETL pipelines.
On the other hand, although Spark SQL has been proven effective parallel query processing engine, some tricks common in traditional databases are not available due to characteristics of storage underneath. TiSpark sits directly on top of a distributed database (TiDB)’s storage engine, expand Spark SQL’s planning with its own extensions and utilizes unique features of database storage engine to achieve functions not possible for Spark SQL on HDFS. With TiSpark, users are able to perform queries directly on changing / fresh data in real time.
The takeaways from this two are twofold:
— How to integrate Spark SQL with a distributed database engine and the benefit of it
— How to leverage Spark SQL’s experimental methods to extend its capacity.
Building a Data Warehouse for Business Analytics using Spark SQL-(Blagoy Kalo...Spark Summit
Blagoy Kaloferov presented on building a data warehouse at Edmunds.com using Spark SQL. He discussed how Spark SQL simplified ETL and enabled business analysts to build data marts more quickly. He showed how Spark SQL was used to optimize a dealer leads dataset in Platfora, reducing build time from hours to minutes. Finally, he proposed an approach using Spark SQL to automate OEM ad revenue billing by modeling complex rules through collaboration between analysts and developers.
Data Warehousing with Spark Streaming at ZalandoDatabricks
Zalandos AI-driven products and distributed landscape of analytical data marts cannot wait for long-running, hard-to-recover, monolithic batch jobs taking all night to calculate already outdated data. Modern data integration pipelines need to deliver fast and easy to consume data sets in high quality. Based on Spark Streaming and Delta, the central data warehousing team was able to deliver widely-used master data as S3 or Kafka streams and snapshots at the same time.
The talk will cover challenges in our fashion data platform and a detailed architectural deep dive about separation of integration from enrichment, providing streams as well as snapshots and feeding the data to distributed data marts. Finally, lessons learned and best practices about Delta’s MERGE command, Scala API vs Spark SQL and schema evolution give more insights and guidance for similar use cases.
Enancing Threat Detection with Big Data and AIDatabricks
Enterprises today face a daily barrage of cyberattacks. Responding quickly to threats is crucial to avoiding a serious breach. To do this successfully, security teams need to monitor and analyze billions of data signals or events each day. They come in different form and format.
Yet, existing security tools are struggling to keep up. Threats are going unnoticed, and remediation timelines are being pushed out. Overcoming these challenges requires a new approach to threat detection rooted in data science.
In this talk, we will discuss the technical aspects of:
* Why cyber security analysis and threat detection are a challenge in the big data era
* How real-time, big data analytics and AI enhance threat detection and uncover anomalous behavior patterns
* Showcase how Data Scientists can use the Databricks’ Unified Analytics Platform to improve an organization's security posture with highly scalable, real-time analytics, and AI while reducing ingress and retention costs
From determining the most convenient rider pickup points to predicting the fastest routes, Uber aims to use data-driven analytics to create seamless trip experiences. Within engineering, analytics inform decision-making processes across the board.
One of the distinct challenges for Uber is analyzing geospatial big data. City locations, trips, and event information, for instance, provide insights that can improve business decisions and better serve users. Geospatial data analysis is particularly challenging, especially in a big data scenario, such as computing how many rides start at a transit location, how many drivers are crossing state lines, and so on. For these analytical requests, we must achieve efficiency, usability, and scalability in order to meet user needs and business requirements.
To accomplish this, we use Hadoop, Hive, and Presto in our production environment to process the big data powering our interactive SQL engine. In this talk, we discuss our engineering effort to optimize geospatial queries in the whole Hadoop stack.
Speakers
Zhenxiao Luo, Engineering Manager, Uber
Lu Niu, Sr Software Engineer, Uber
This document provides an agenda for a hands-on introduction and hackathon kickoff for Apache Geode. The agenda includes details about the hackathon, an introduction to Apache Geode including its history, key features, and roadmap. It also covers hands-on labs for building, running, and clustering Geode as well as creating a first application. The document concludes with information on how to contribute to the Geode project.
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...Databricks
Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a “pure SaaS” company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems.
As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark.
In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism’s Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support interactive user experience.
Finally, we will share the future challenges for 2018 and beyond.
Pandas UDF: Scalable Analysis with Python and PySparkLi Jin
Over the past few years, Python has become the default language for data scientists. Packages such as pandas, numpy, statsmodel, and scikit-learn have gained great adoption and become the mainstream toolkits. At the same time, Apache Spark has become the de facto standard in processing big data. Spark ships with a Python interface, aka PySpark, however, because Spark’s runtime is implemented on top of JVM, using PySpark with native Python library sometimes results in poor performance and usability.
In this talk, we introduce a new type of PySpark UDF designed to solve this problem – Vectorized UDF. Vectorized UDF is built on top of Apache Arrow and bring you the best of both worlds – the ability to define easy to use, high performance UDFs and scale up your analysis with Spark.
Sherlock: an anomaly detection service on top of Druid DataWorks Summit
Sherlock is an anomaly detection service built on top of Druid. It leverages EGADS (Extensible Generic Anomaly Detection System; github.com/yahoo/egads) to detect anomalies in time-series data. Users can schedule jobs on an hourly, daily, weekly, or monthly basis, view anomaly reports from Sherlock's interface, or receive them via email.
Sherlock has four major components: timeseries generation, EGADS anomaly detection, Redis backend and Spark Java UI. Timeseries generation involves building, validating, querying, parsing the Druid query. Parsed Druid response is then fed to EGADS anomaly detection component which detects and generates the anomaly reports for each input time-series data. Sherlock uses Redis backend to store jobs metadata, generated anomaly reports and persistent job queue for scheduling jobs, etc. Users can choose to have a clustered Redis or standalone Redis. Sherlock provides user interface built with Spark Java. The UI enables users to submit instant anomaly analysis, create, and launch detection jobs, view anomalies on a heatmap and on a graph. Jigarkumar Patel, Software Development Engineer I, Oath Inc. and, David Servose, Software Systems Engineer, Oath
Secured (Kerberos-based) Spark Notebook for Data Science: Spark Summit East t...Spark Summit
The document discusses securing Spark notebooks for data science by integrating Kerberos authentication. It begins with an overview of Spark notebooks and the current authentication approach. It then covers the requirements for Kerberos integration, how Kerberos works in HDFS and Yarn clusters, and a proposed design to integrate Kerberos into JupyterHub, SparkMagic and Livy to authenticate users and allow secured access to HDFS and Spark from notebooks. Key aspects of the design include custom JupyterHub authenticators and spawners, obtaining service tickets from the KDC, and propagating user identities through the system.
Big Data Meets Learning Science: Keynote by Al EssaSpark Summit
How do we learn and how can we learn better? Educational technology is undergoing a revolution fueled by learning science and data science. The promise is to make a high-quality personalized education accessible and affordable by all. In this presentation Alfred will describe how Apache Spark and Databricks are at the center of the innovation pipeline at McGraw Hill for developing next-generation learner models and algorithms in support of millions of learners and instructors worldwide.
Uber has created a Data Science Workbench to improve the productivity of its data scientists by providing scalable tools, customization, and support. The Workbench provides Jupyter notebooks for interactive coding and visualization, RStudio for rapid prototyping, and Apache Spark for distributed processing. It aims to centralize infrastructure provisioning, leverage Uber's distributed backend, enable knowledge sharing and search, and integrate with Uber's data ecosystem tools. The Workbench manages Docker containers of tools like Jupyter and RStudio running on a Mesos cluster, with files stored in a shared file system. It addresses the problems of wasted time from separate infrastructures and lack of tool standardization across Uber's data science teams.
The challenge of computing big data for evolving digital business processes demands variety of computation techniques and engines (SQL, OLAP, time-series, graph, document store), but working in unified framework. A simple architecture of data transformations while ensuring the security, governance, and operational administration are the necessary critical components for enterprise production environments supporting day-to-day business processes. In this session, you will learn about best practices & critical components to ensure business value from latest production deployments. Hear how existing customers are using SAP Vora and the value they have achieved so far with this in-memory engine for distributed data processing. The session provides you with a clear understanding how SAP Vora and open source components like Apache Hadoop and Apache Spark offer an architecture that supports a wide variety of use cases and industries. You will also receive very useful insight where to find development resources, test drive demos, and general documentation.
Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...Spark Summit
In many cases, Big Data becomes just another buzzword because of the lack of tools that can support both the technological requirements for developing and deploying of the projects and/or the fluency of communication between the different profiles of people involved in the projects.
In this talk, we will present Moriarty, a set of tools for fast prototyping of Big Data applications that can be deployed in an Apache Spark environment. These tools support the creation of Big Data workflows using the already existing functional blocks or supporting the creation of new functional blocks. The created workflow can then be deployed in a Spark infrastructure and used through a REST API.
For better understanding of Moriarty, the prototyping process and the way it hides the Spark environment to the Big Data users and developers, we will present it together with a couple of examples based on a Industry 4.0 success cases and other on a logistic success case.
Data Con LA 2020
Description
Apache Druid is a cloud-native open-source database that enables developers to build highly-scalable, low-latency, real-time interactive dashboards and apps to explore huge quantities of data. This column-oriented database provides the microsecond query response times required for ad-hoc queries and programmatic analytics. Druid natively streams data from Apache Kafka (and more) and batch loads just about anything. At ingestion, Druid partitions data based on time so time-based queries run significantly faster than traditional databases, plus Druid offers SQL compatibility. Druid is used in production by AirBnB, Nielsen, Netflix and more for real-time and historical data analytics. This talk provides an introduction to Apache Druid including: Druid's core architecture and its advantages, Working with streaming and batch data in Druid, Querying data and building apps on Druid and Real-world examples of Apache Druid in action
Speaker
Matt Sarrel, Imply Data, Developer Evangelist
This document discusses the history and development of Python data analysis tools, including pandas. It covers Wes McKinney's work on pandas from 2008 to the present, including the motivations for making data analysis easier and more productive. It also summarizes the development of related projects like Apache Arrow for standardizing columnar data representations to improve code reuse across languages.
Using Apache Arrow, Calcite, and Parquet to Build a Relational CacheDremio Corporation
From DataEngConf 2017 - Everybody wants to get to data faster. As we move from more general solution to specific optimization techniques, the level of performance impact grows. This talk will discuss how layering in-memory caching, columnar storage and relational caching can combine to provide a substantial improvement in overall data science and analytical workloads. It will include a detailed overview of how you can use Apache Arrow, Calcite and Parquet to achieve multiple magnitudes improvement in performance over what is currently possible.
The Future of Column-Oriented Data Processing With Apache Arrow and Apache Pa...Dremio Corporation
Essentially every successful analytical DBMS in the market today makes use of column-oriented data structures. In the Hadoop ecosystem, Apache Parquet (and Apache ORC) provide similar advantages in terms of processing and storage efficiency. Apache Arrow is the in-memory counterpart to these formats and has been been embraced by over a dozen open source projects as the de facto standard for in-memory processing. In this session the PMC Chair for Apache Arrow and the PMC Chair for Apache Parquet discuss the future of column-oriented processing.
At ING we needed a way to implement Data science models from exploration into production. I will do this talk from my experience on the exploration and production Hadoop environment as a senior Ops engineer. For this we are using OpenShift to run Docker containers that connect to the big data Hadoop environment.
During this talk I will explain why we need this and how this is done at ING. Also how to set up a docker container running a data science model using Hive, Python, and Spark. I’ll explain how to use Docker files to build Docker images, add all the needed components inside the Docker image, and how to run different versions of software in different containers.
In the end I will also give a demo of how it runs and is automated using Git with webhook connecting to Jenkins and start the docker service that will connect to a big data Hadoop environment.
This is going to be a great technical talk for engineers and data scientist.
Speaker
Lennard Cornelis, Ops Engineer, ING
Adding structure to your streaming pipelines: moving from Spark streaming to ...DataWorks Summit
How do you go from a strictly typed object-based streaming pipeline with simple operations to a structured streaming pipeline with higher order complex relational operations? This is what the Data Engineering team did at GoPro to scale up the development of streaming pipelines for the rapidly growing number of devices and applications.
When big data frameworks such as Hadoop first came to exist, developers were happy because we could finally process large amounts of data without writing complex multi-threaded code or worse yet writing complicated distributed code. Unfortunately, only very simple operations were available such as map and reduce. Almost immediately, higher level operations were desired similar to relational operations. And so Hive and dozens (hundreds?) of SQL-based big data tools became available for more developer-efficient batch processing of massive amounts of data.
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world, so that nearly every streaming framework now supports higher level relational operations.
In this talk, we will discuss in a very hands-on manner how the streaming data pipelines for GoPro devices and apps have moved from the original Spark streaming with its simple RDD-based operations in Spark 1.x to Spark's structured streaming with its higher level relational operations in Spark 2.x. We will talk about the differences, advantages, and necessary pain points that must be addressed in order to scale relational-based streaming pipelines for massive IoT streams. We will also talk about moving from “hand built” Hadoop/Spark clusters running in the cloud to using a Spark-based cloud service. DAVID WINTERS, Big Data Architect, GoPro and HAO ZOU, Senior Software Engineer, GoPro
When Apache Spark Meets TiDB with Xiaoyu MaDatabricks
During the past 10 years, big-data storage layers mainly focus on analytical use cases. When it comes to analytical cases, users usually offload data onto Hadoop cluster and perform queries on HDFS files. People struggle dealing with modifications on append only storage and maintain fragile ETL pipelines.
On the other hand, although Spark SQL has been proven effective parallel query processing engine, some tricks common in traditional databases are not available due to characteristics of storage underneath. TiSpark sits directly on top of a distributed database (TiDB)’s storage engine, expand Spark SQL’s planning with its own extensions and utilizes unique features of database storage engine to achieve functions not possible for Spark SQL on HDFS. With TiSpark, users are able to perform queries directly on changing / fresh data in real time.
The takeaways from this two are twofold:
— How to integrate Spark SQL with a distributed database engine and the benefit of it
— How to leverage Spark SQL’s experimental methods to extend its capacity.
Building a Data Warehouse for Business Analytics using Spark SQL-(Blagoy Kalo...Spark Summit
Blagoy Kaloferov presented on building a data warehouse at Edmunds.com using Spark SQL. He discussed how Spark SQL simplified ETL and enabled business analysts to build data marts more quickly. He showed how Spark SQL was used to optimize a dealer leads dataset in Platfora, reducing build time from hours to minutes. Finally, he proposed an approach using Spark SQL to automate OEM ad revenue billing by modeling complex rules through collaboration between analysts and developers.
Data Warehousing with Spark Streaming at ZalandoDatabricks
Zalandos AI-driven products and distributed landscape of analytical data marts cannot wait for long-running, hard-to-recover, monolithic batch jobs taking all night to calculate already outdated data. Modern data integration pipelines need to deliver fast and easy to consume data sets in high quality. Based on Spark Streaming and Delta, the central data warehousing team was able to deliver widely-used master data as S3 or Kafka streams and snapshots at the same time.
The talk will cover challenges in our fashion data platform and a detailed architectural deep dive about separation of integration from enrichment, providing streams as well as snapshots and feeding the data to distributed data marts. Finally, lessons learned and best practices about Delta’s MERGE command, Scala API vs Spark SQL and schema evolution give more insights and guidance for similar use cases.
Enancing Threat Detection with Big Data and AIDatabricks
Enterprises today face a daily barrage of cyberattacks. Responding quickly to threats is crucial to avoiding a serious breach. To do this successfully, security teams need to monitor and analyze billions of data signals or events each day. They come in different form and format.
Yet, existing security tools are struggling to keep up. Threats are going unnoticed, and remediation timelines are being pushed out. Overcoming these challenges requires a new approach to threat detection rooted in data science.
In this talk, we will discuss the technical aspects of:
* Why cyber security analysis and threat detection are a challenge in the big data era
* How real-time, big data analytics and AI enhance threat detection and uncover anomalous behavior patterns
* Showcase how Data Scientists can use the Databricks’ Unified Analytics Platform to improve an organization's security posture with highly scalable, real-time analytics, and AI while reducing ingress and retention costs
From determining the most convenient rider pickup points to predicting the fastest routes, Uber aims to use data-driven analytics to create seamless trip experiences. Within engineering, analytics inform decision-making processes across the board.
One of the distinct challenges for Uber is analyzing geospatial big data. City locations, trips, and event information, for instance, provide insights that can improve business decisions and better serve users. Geospatial data analysis is particularly challenging, especially in a big data scenario, such as computing how many rides start at a transit location, how many drivers are crossing state lines, and so on. For these analytical requests, we must achieve efficiency, usability, and scalability in order to meet user needs and business requirements.
To accomplish this, we use Hadoop, Hive, and Presto in our production environment to process the big data powering our interactive SQL engine. In this talk, we discuss our engineering effort to optimize geospatial queries in the whole Hadoop stack.
Speakers
Zhenxiao Luo, Engineering Manager, Uber
Lu Niu, Sr Software Engineer, Uber
This document provides an agenda for a hands-on introduction and hackathon kickoff for Apache Geode. The agenda includes details about the hackathon, an introduction to Apache Geode including its history, key features, and roadmap. It also covers hands-on labs for building, running, and clustering Geode as well as creating a first application. The document concludes with information on how to contribute to the Geode project.
This document discusses machine learning techniques in Spark including classification, clustering, and collaborative filtering. It provides examples of building classification models with Spark including vectorizing data, training models, evaluating models, and making predictions. Clustering and collaborative filtering are also introduced. The document demonstrates collaborative filtering with Spark using alternating least squares to build a recommendation model from user ratings data.
The document contains examples of time series data in various formats:
- Observations with a timestamp, key, and value
- Instants with timestamps and values for keys A, B, and C
- A time series DataFrame with a DateTimeIndex and values for keys A, B, and C
It also shows examples of working with time series data in Spark using TimeSeriesRDDs, including slicing data, filling in missing values, removing serial correlations, and fitting ARIMA and GARCH models.
An introduction to Spark MLlib from the Apache Spark with Scala course available at https://www.supergloo.com/fieldnotes/portfolio/apache-spark-scala/. These slides present an overview on machine learning with Apache Spark MLlib.
For more background on machine learning see my other uploaded presentation "Machine Learning with Spark".
This document summarizes the history and ongoing development of MLlib, Spark's machine learning library. MLlib was initially developed by the MLbase team in 2013 and has since grown significantly with over 80 contributors. It provides algorithms for classification, regression, clustering, collaborative filtering, and linear algebra/optimization. Recent improvements include new algorithms like random forests, pipelines for simplified ML workflows, and continued performance gains.
Large-Scale Machine Learning with Apache SparkDB Tsai
Spark is a new cluster computing engine that is rapidly gaining popularity — with over 150 contributors in the past year, it is one of the most active open source projects in big data, surpassing even Hadoop MapReduce. Spark was designed to both make traditional MapReduce programming easier and to support new types of applications, with one of the earliest focus areas being machine learning. In this talk, we’ll introduce Spark and show how to use it to build fast, end-to-end machine learning workflows. Using Spark’s high-level API, we can process raw data with familiar libraries in Java, Scala or Python (e.g. NumPy) to extract the features for machine learning. Then, using MLlib, its built-in machine learning library, we can run scalable versions of popular algorithms. We’ll also cover upcoming development work including new built-in algorithms and R bindings.
Bio:
Xiangrui Meng is a software engineer at Databricks. He has been actively involved in the development of Spark MLlib since he joined. Before Databricks, he worked as an applied research engineer at LinkedIn, where he was the main developer of an offline machine learning framework in Hadoop MapReduce. His thesis work at Stanford is on randomized algorithms for large-scale linear regression.
Combining Machine Learning Frameworks with Apache SparkDatabricks
This document discusses combining machine learning frameworks with Apache Spark. It provides an overview of Apache Spark and MLlib, describes how to distribute TensorFlow computations using Spark, and discusses managing machine learning workflows with Spark through features like cross validation, persistence, and distributed data sources. The goal is to make machine learning easy, scalable, and integrate with existing workflows.
Machine learning is overhyped nowadays. There is a strong belief that this area is exclusively for data scientists with a deep mathematical background that leverage Python (scikit-learn, Theano, Tensorflow, etc.) or R ecosystem and use specific tools like Matlab, Octave or similar. Of course, there is a big grain of truth in this statement, but we, Java engineers, also can take the best of machine learning universe from an applied perspective by using our native language and familiar frameworks like Apache Spark. During this introductory presentation, you will get acquainted with the simplest machine learning tasks and algorithms, like regression, classification, clustering, widen your outlook and use Apache Spark MLlib to distinguish pop music from heavy metal and simply have fun.
Source code: https://github.com/tmatyashovsky/spark-ml-samples
Design by Yarko Filevych: http://filevych.com/
This document provides an overview of Apache Spark's MLlib machine learning library. It discusses machine learning concepts and terminology, the types of machine learning techniques supported by MLlib like classification, regression, clustering, collaborative filtering and dimensionality reduction. It covers MLlib's algorithms, data types, feature extraction and preprocessing capabilities. It also provides tips for using MLlib such as preparing features, configuring algorithms, caching data, and avoiding overfitting. Finally, it introduces ML Pipelines for constructing machine learning workflows in Spark.
Practical Machine Learning Pipelines with MLlibDatabricks
This talk from 2015 Spark Summit East discusses Pipelines and related concepts introduced in Spark 1.2 which provide a simple API for users to set up complex ML workflows.
Presented by David Taieb, Architect, IBM Cloud Data Services
Along with Spark Streaming, Spark SQL and GraphX, MLLib is one of the four key architectural components of Spark. It provides easy-to-use (even for beginners), powerful Machine Learning APIs that are designed to work in parallel using Spark RDDs. In this session, we’ll introduce the different algorithms available in MLLib, e.g. supervised learning with classification (binary and multi class) and regression but also unsupervised learning with clustering (K-means) and recommendation systems. We’ll conclude the presentation with a deep dive on a sample machine learning application built with Spark MLLib that predicts whether a scheduled flight will be delayed or not. This application trains a model using data from real flight information. The labeled flight data is combined with weather data from the “Insight for Weather” service available on IBM Bluemix Cloud Platform to form the training, test and blind data. Even if you are not a black belt in machine learning, you will learn in this session how to leverage powerful Machine Learning algorithms available in Spark to build interesting predictive and prescriptive applications.
About the Speaker: For the last 4 years, David has been the lead architect for the Watson Core UI & Tooling team based in Littleton, Massachusetts. During that time, he led the design and development of a Unified Tooling Platform to support all the Watson Tools including accuracy analysis, test experiments, corpus ingestion, and training data generation. Before that, he was the lead architect for the Domino Server OSGi team responsible for integrating the eXpeditor J2EE Web Container in Domino and building first class APIs for the developer community. He started with IBM in 1996, working on various globalization technologies and products including Domino Global Workbench (used to develop multilingual Notes/Domino NSF applications) and a multilingual Content Management system for the Websphere Application Server. David enjoys sharing his experience by speaking at conferences. You’ll find him at various events like the Unicode conference, Eclipsecon, and Lotusphere. He’s also passionate about building tools that help improve developer productivity and overall experience.
This document provides an overview of machine learning concepts and techniques using Apache Spark. It begins with introducing machine learning and describing supervised and unsupervised learning. Then it discusses Spark and how it can be used for large-scale machine learning tasks through its MLlib library and GraphX API. Several examples of machine learning applications are presented, such as classification, regression, clustering, and graph analytics. The document concludes with demonstrating machine learning algorithms in Spark.
Data Science - Part X - Time Series ForecastingDerek Kane
This lecture provides an overview of Time Series forecasting techniques and the process of creating effective forecasts. We will go through some of the popular statistical methods including time series decomposition, exponential smoothing, Holt-Winters, ARIMA, and GLM Models. These topics will be discussed in detail and we will go through the calibration and diagnostics effective time series models on a number of diverse datasets.
Apache Spark: Lightning Fast Cluster ComputingAll Things Open
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.
피보탈 클라우드 파운더리를 사용하여 애플리케이션과 관련 서비스를 배포하고 운용하는 것에 대한 장점 및 이를 멀티 클라우드에서 사용할 수 있는 방법에 대해 설명된 슬라이드 입니다. 마이크로 서비스와 클라우드, 애플리케이션의 서비스 연동에 어떤 내용이 중요한지에 대해 개괄적으로 설명 됩니다.
Keynote presentation for the Pivotal Cloud Platform Roadshow. Introduces the market drivers for the Cloud Foundry Platform as a Service, discusses open source softwared (Cloud Foundry is OSS) and introduces the fundamentals of the platform.
This document provides an overview of Spring XD, which allows for ingesting, processing, and exporting streaming and batch data. Some key points:
- Spring XD provides modules for sources, processors, and sinks to build streams for ingesting data from various sources and exporting to various systems. It also supports batch jobs.
- Core concepts include modules, streams, taps, and jobs. Streams are composed of sources, processors, and sinks. Taps dynamically add listeners. Jobs provide ETL and workflow capabilities.
- Spring XD supports ingesting from sources like Kafka, files, databases. It can process data in real-time or using batch and export to systems like HDFS, databases.
Logging infrastructure for Microservices using StreamSets Data CollectorCask Data
This document discusses using StreamSets Data Collector (SDC) to build a logging infrastructure for microservices. SDC can ingest logs from microservices running in containers and handle issues like schema changes and new log formats. It processes and transforms the logs, sending them to destinations like Kafka. SDC pipelines can run on Spark clusters on Yarn and Mesos to handle large volumes of log data and load it into systems like HDFS, HBase and Elasticsearch for analysis.
Building Cloud Native Applications with Oracle Autonomous Database.Oracle Developers
This document discusses building cloud native applications with Oracle Autonomous Database. It provides an overview of:
1) The evolution of computing and development from monolithic to cloud native applications.
2) The challenges of managing databases with microservices, and how Oracle Autonomous Database can serve as a single database for all development needs.
3) How to build, deploy, and manage cloud native applications using Oracle Cloud Infrastructure services like the Container Engine for Kubernetes, Functions, and the Autonomous Transaction Processing database.
Apache Spark with Java 8 training covers the basics of Apache Spark including its features like speed, support for multiple languages, and advanced analytics capabilities. It also covers Spark concepts like RDDs, DataFrames, and Spark SQL. The training discusses how Java 8 features like lambda expressions improve Spark development. It teaches Spark programming concepts and how to develop Spark applications and run them on clusters.
https://www.learntek.org/apache-spark-with-java/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
According to Gartner, organizations can reduce their database spend by up to 80% by deploying EDB Postgres in place of traditional database solutions like Oracle. Nevertheless, the perceived risks associated with migrating from Oracle to an open source-based alternative prevents many organizations from trying.
Review this presentation to learn some of EDB Postgres Enterprise’s more important features and techniques employed to reduce migration risk.
This presentation will be valuable to organizations researching Postgres, as well as current Oracle customers considering migrating to an open source-based database management system such as EDB Postgres. It highlights key points for both business and technical decision-makers and influencers.
Pivotal Digital Transformation Forum: Data Science Technical OverviewVMware Tanzu
This document provides an overview of Pivotal's data science capabilities and tools. It discusses how Pivotal uses an Agile approach to data science projects, focusing on frequent interactions with customers. Pivotal's software stack is designed to enable data science work by supporting real-time, interactive, and batch operations on data through tools like Spring XD, Pivotal HD, and GemFire. Examples are provided of how Pivotal has used these tools for applications like connected cars and scalable video analytics.
Introducing Apache Geode and Spring Data GemFireJohn Blum
This document introduces Apache Geode, an open source distributed in-memory data management platform. It discusses what Geode is, how it is implemented, and some key features like high availability, scalability and low latency. It also introduces Spring Data GemFire, which simplifies using Geode with Spring applications through features like repositories and caching. Finally, it outlines the project roadmap and opportunities to get involved in the Geode community.
Overview and Walkthrough of the Application Programming Model with SAP Cloud ...SAP Cloud Platform
Learn how to seamlessly combine open-source and cloud-native software with SAP technologies into a consistent, end-to-end programming model and development experience that guides application developers with best practices and relieves them from tedious boilerplate tasks, enabling them to focus on solving their domain problems. Get an overview of the key technologies and tools as well as an end-to-end walkthrough of developing business services and applications.
The document discusses the evolution of Pivotal Gemfire, now known as Apache Geode, from a proprietary product to an open source project. It provides an overview of Gemfire/Geode's capabilities including elastic scalability, high performance, and flexibility for developers. It also outlines Geode's role as a potential in-memory data exchange layer and integration point across modern data infrastructure technologies. Key aspects of Geode like its PDX serialization and asynchronous events are highlighted as building blocks that position it well for this role.
It’s no longer a world of just relational databases. Companies are increasingly adopting specialized datastores such as Hadoop, HBase, MongoDB, Elasticsearch, Solr and S3. Apache Drill, an open source, in-memory, columnar SQL execution engine, enables interactive SQL queries against more datastores.
This document discusses connecting internet of things (IoT) devices to business intelligence (BI) systems. It describes how IoT data from devices like connected cars, smart homes and cities can be analyzed in real-time for operational efficiency, predictive maintenance and self-driving vehicles. The document outlines an example use case of connecting a Raspberry Pi to a vehicle's OBD-II port to log driving data and integrate it with MapR's distributed database platform for real-time analytics and visualization with Grafana and QlikView. It also discusses extending this to optimize home heating/cooling using IoT thermostats like Nest based on vehicle location data.
This document contains a presentation on MySQL Workbench. It discusses MySQL Workbench 6.3, its advanced features like the SQL IDE, modeling, administration capabilities. It also discusses how Workbench can be used to do more through Python scripting, SSH tunneling and Fabric. Finally, it outlines some new features in MySQL 5.7 RC like support for JSON, GIS and other enhancements.
Removing Barriers Between Dev and Ops by Shahaf Airily, Advisory Field Engineer EMEA, Pivotal. This presentation is from VMworld Barcelona. For more information, visit https://pivotal.io/event/vmworld-europe
Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake...NoSQLmatters
Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.
Similar to Building a Stock Prediction system with Machine Learning using Geode, SpringXD and Spark MLLib (20)
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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!
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Mobile app Development Services | Drona InfotechDrona Infotech
Drona Infotech is one of the Best Mobile App Development Company In Noida Maintenance and ongoing support. mobile app development Services can help you maintain and support your app after it has been launched. This includes fixing bugs, adding new features, and keeping your app up-to-date with the latest
Visit Us For :
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.