This document provides an overview of data pipelines and various technologies that can be used to build them. It begins with a brief history of pipelines and their origins in UNIX. It then discusses common pipeline concepts like decoupling of tasks, encapsulation of processing, and reuse of tasks. Several examples of graphical and programmatic pipeline solutions are presented, including Luigi, Piecepipe, Spring Batch, and workflow engines. Big data pipelines using Hadoop and technologies like Pig and Oozie are also covered. Finally, cloud-based pipeline technologies from AWS like Kinesis, Data Pipeline, Lambda, and EMR are described. Throughout the document, examples are provided to illustrate how different technologies can be used to specify and run data processing pipelines.
Designing and Building a Graph Database Application - Ian Robinson (Neo Techn...jaxLondonConference
Presented at JAX London
In this session we'll look at some of the design and implementation strategies you can employ when building a Neo4j-based graph database solution, including architectural choices, data modelling, and testing.
Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks
In this webcast, Patrick Wendell from Databricks will be speaking about Apache Spark's new 1.6 release.
Spark 1.6 will include (but not limited to) a type-safe API called Dataset on top of DataFrames that leverages all the work in Project Tungsten to have more robust and efficient execution (including memory management, code generation, and query optimization) [SPARK-9999], adaptive query execution [SPARK-9850], and unified memory management by consolidating cache and execution memory [SPARK-10000].
Designing and Building a Graph Database Application - Ian Robinson (Neo Techn...jaxLondonConference
Presented at JAX London
In this session we'll look at some of the design and implementation strategies you can employ when building a Neo4j-based graph database solution, including architectural choices, data modelling, and testing.
Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks
In this webcast, Patrick Wendell from Databricks will be speaking about Apache Spark's new 1.6 release.
Spark 1.6 will include (but not limited to) a type-safe API called Dataset on top of DataFrames that leverages all the work in Project Tungsten to have more robust and efficient execution (including memory management, code generation, and query optimization) [SPARK-9999], adaptive query execution [SPARK-9850], and unified memory management by consolidating cache and execution memory [SPARK-10000].
Frustration-Reduced PySpark: Data engineering with DataFramesIlya Ganelin
In this talk I talk about my recent experience working with Spark Data Frames in Python. For DataFrames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics.
A lot of data scientists use the python library pandas for quick exploration of data. The most useful construct in pandas (based on R, I think) is the dataframe, which is a 2D array(aka matrix) with the option to “name” the columns (and rows). But pandas is not distributed, so there is a limit on the data size that can be explored.
Spark is a great map-reduce like framework that can handle very big data by using a shared nothing cluster of machines.
This work is an attempt to provide a pandas-like DSL on top of spark, so that data scientists familiar with pandas have a very gradual learning curve.
Lens: Data exploration with Dask and Jupyter widgetsVíctor Zabalza
The first step in any data-intensive project is understanding the available data. To this end, data scientists spend a significant part of their time carrying out data quality assessments and data exploration. In spite of this being a crucial step, it usually requires repeating a series of menial tasks before the data scientist gains an understanding of the dataset and can progress to the next steps in the project.
In this talk I will present Lens (https://github.com/asidatascience/lens), a Python package which automates this drudge work, enables efficient data exploration, and kickstarts data science projects. A summary is generated for each dataset, including:
- General information about the dataset, including data quality of each of the columns;
- Distribution of each of the columns through statistics and plots (histogram, CDF, KDE), optionally grouped by other categorical variables;
- 2D distribution between pairs of columns;
- Correlation coefficient matrix for all numerical columns.
Building this tool has provided a unique view into the full Python data stack, from the parallelised analysis of a dataframe within a Dask custom execution graph, to the interactive visualisation with Jupyter widgets and Plotly. During the talk, I will also introduce how Dask works, and demonstrate how to migrate data pipelines to take advantage of its scalable capabilities.
AUTOMATED DATA EXPLORATION - Building efficient analysis pipelines with DaskVíctor Zabalza
# Talk given at PyCon UK 2017
The first step in any data-intensive project is understanding the available data. To this end, data scientists spend a significant part of their time carrying out data quality assessments and data exploration. In spite of this being a crucial step, it usually requires repeating a series of menial tasks before the data scientist gains an understanding ofthe dataset and can progress to the next steps in the project.
In this talk I will detail the inner workings of a Python package that we have built which automates this drudge work, enables efficient data exploration, and kickstarts data science projects. A summary is generated for each dataset, including:
- General information about the dataset, including data quality of each of the columns;
- Distribution of each of the columns through statistics and plots (histogram, CDF, KDE), optionally grouped by other categorical variables;
- 2D distribution between pairs of columns;
- Correlation coefficient matrix for all numerical columns.
Building this tool has provided a unique view into the full Python data stack, from the parallelised analysis of a dataframe within a Dask custom execution graph, to the interactive visualisation with Jupyter widgets and Plotly. During the talk, I will also introduce how Dask works, and demonstrate how to migrate data pipelines to take advantage of its scalable capabilities.
SparkSQL: A Compiler from Queries to RDDsDatabricks
SparkSQL, a module for processing structured data in Spark, is one of the fastest SQL on Hadoop systems in the world. This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will walk away with a deeper understanding of how Spark analyzes, optimizes, plans and executes a user’s query.
Speaker: Sameer Agarwal
This talk was originally presented at Spark Summit East 2017.
Jump Start on Apache® Spark™ 2.x with Databricks Databricks
Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands On Labs
You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL.
Level: Beginner to intermediate, not for advanced Spark users.
Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional.
Bio:
Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.
Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark.
These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
In this talk at 2015 Spark Summit East, the lead developer of Spark streaming, @tathadas, talks about the state of Spark streaming:
Spark Streaming extends the core Apache Spark API to perform large-scale stream processing, which is revolutionizing the way Big “Streaming” Data application are being written. It is rapidly adopted by companies spread across various business verticals – ad and social network monitoring, real-time analysis of machine data, fraud and anomaly detections, etc. These companies are mainly adopting Spark Streaming because – Its simple, declarative batch-like API makes large-scale stream processing accessible to non-scientists. – Its unified API and a single processing engine (i.e. Spark core engine) allows a single cluster and a single set of operational processes to cover the full spectrum of uses cases – batch, interactive and stream processing. – Its stronger, exactly-once semantics makes it easier to express and debug complex business logic. In this talk, I am going to elaborate on such adoption stories, highlighting interesting use cases of Spark Streaming in the wild. In addition, this presentation will also showcase the exciting new developments in Spark Streaming and the potential future roadmap.
Large scale, interactive ad-hoc queries over different datastores with Apache...jaxLondonConference
Presented at JAX London 2013
Apache Drill is a distributed system for interactive ad-hoc query and analysis of large-scale datasets. It is the Open Source version of Google’s Dremel technology. Apache Drill is designed to scale to thousands of servers and able to process Petabytes of data in seconds, enabling SQL-on-Hadoop and supporting a variety of data sources.
Spark Application Carousel: Highlights of Several Applications Built with SparkDatabricks
This talk from 2015 Spark Summit East covers 3 applications built with Apache Spark:
1. Web Logs Analysis: Basic Data Pipeline - Spark & Spark SQL
2. Wikipedia Dataset Analysis: Machine Learning
3. Facebook API: Graph Algorithms
Unified Big Data Processing with Apache Spark (QCON 2014)Databricks
While early big data systems, such as MapReduce, focused on batch processing, the demands on these systems have quickly grown. Users quickly needed to run (1) more interactive ad-hoc queries, (2) sophisticated multi-pass algorithms (e.g. machine learning), and (3) real-time stream processing. The result has been an explosion of specialized systems to tackle these new workloads. Unfortunately, this means more systems to learn, manage, and stitch together into pipelines. Spark is unique in taking a step back and trying to provide a *unified* post-MapReduce programming model that tackles all these workloads. By generalizing MapReduce to support fast data sharing and low-latency jobs, we achieve best-in-class performance in a variety of workloads, while providing a simple programming model that lets users easily and efficiently combine them.
Today, Spark is the most active open source project in big data, with high activity in both the core engine and a growing array of standard libraries built on top (e.g. machine learning, stream processing, SQL). I'm going to talk about the latest developments in Spark and show examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code.
Talk by Databricks CTO and Apache Spark creator Matei Zaharia at QCON San Francisco 2014.
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
Frustration-Reduced PySpark: Data engineering with DataFramesIlya Ganelin
In this talk I talk about my recent experience working with Spark Data Frames in Python. For DataFrames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics.
A lot of data scientists use the python library pandas for quick exploration of data. The most useful construct in pandas (based on R, I think) is the dataframe, which is a 2D array(aka matrix) with the option to “name” the columns (and rows). But pandas is not distributed, so there is a limit on the data size that can be explored.
Spark is a great map-reduce like framework that can handle very big data by using a shared nothing cluster of machines.
This work is an attempt to provide a pandas-like DSL on top of spark, so that data scientists familiar with pandas have a very gradual learning curve.
Lens: Data exploration with Dask and Jupyter widgetsVíctor Zabalza
The first step in any data-intensive project is understanding the available data. To this end, data scientists spend a significant part of their time carrying out data quality assessments and data exploration. In spite of this being a crucial step, it usually requires repeating a series of menial tasks before the data scientist gains an understanding of the dataset and can progress to the next steps in the project.
In this talk I will present Lens (https://github.com/asidatascience/lens), a Python package which automates this drudge work, enables efficient data exploration, and kickstarts data science projects. A summary is generated for each dataset, including:
- General information about the dataset, including data quality of each of the columns;
- Distribution of each of the columns through statistics and plots (histogram, CDF, KDE), optionally grouped by other categorical variables;
- 2D distribution between pairs of columns;
- Correlation coefficient matrix for all numerical columns.
Building this tool has provided a unique view into the full Python data stack, from the parallelised analysis of a dataframe within a Dask custom execution graph, to the interactive visualisation with Jupyter widgets and Plotly. During the talk, I will also introduce how Dask works, and demonstrate how to migrate data pipelines to take advantage of its scalable capabilities.
AUTOMATED DATA EXPLORATION - Building efficient analysis pipelines with DaskVíctor Zabalza
# Talk given at PyCon UK 2017
The first step in any data-intensive project is understanding the available data. To this end, data scientists spend a significant part of their time carrying out data quality assessments and data exploration. In spite of this being a crucial step, it usually requires repeating a series of menial tasks before the data scientist gains an understanding ofthe dataset and can progress to the next steps in the project.
In this talk I will detail the inner workings of a Python package that we have built which automates this drudge work, enables efficient data exploration, and kickstarts data science projects. A summary is generated for each dataset, including:
- General information about the dataset, including data quality of each of the columns;
- Distribution of each of the columns through statistics and plots (histogram, CDF, KDE), optionally grouped by other categorical variables;
- 2D distribution between pairs of columns;
- Correlation coefficient matrix for all numerical columns.
Building this tool has provided a unique view into the full Python data stack, from the parallelised analysis of a dataframe within a Dask custom execution graph, to the interactive visualisation with Jupyter widgets and Plotly. During the talk, I will also introduce how Dask works, and demonstrate how to migrate data pipelines to take advantage of its scalable capabilities.
SparkSQL: A Compiler from Queries to RDDsDatabricks
SparkSQL, a module for processing structured data in Spark, is one of the fastest SQL on Hadoop systems in the world. This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will walk away with a deeper understanding of how Spark analyzes, optimizes, plans and executes a user’s query.
Speaker: Sameer Agarwal
This talk was originally presented at Spark Summit East 2017.
Jump Start on Apache® Spark™ 2.x with Databricks Databricks
Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands On Labs
You will use Databricks Community Edition, which will give you unlimited free access to a ~6 GB Spark 2.x local mode cluster. And in the process, you will learn how to create a cluster, navigate in Databricks, explore a couple of datasets, perform transformations and ETL, save your data as tables and parquet files, read from these sources, and analyze datasets using DataFrames/Datasets API and Spark SQL.
Level: Beginner to intermediate, not for advanced Spark users.
Prerequisite: You will need a laptop with Chrome or Firefox browser installed with at least 8 GB. Introductory or basic knowledge Scala or Python is required, since the Notebooks will be in Scala; Python is optional.
Bio:
Jules S. Damji is an Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, LoudCloud/Opsware, VeriSign, Scalix, and ProQuest, building large-scale distributed systems. Before joining Databricks, he was a Developer Advocate at Hortonworks.
Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark.
These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
In this talk at 2015 Spark Summit East, the lead developer of Spark streaming, @tathadas, talks about the state of Spark streaming:
Spark Streaming extends the core Apache Spark API to perform large-scale stream processing, which is revolutionizing the way Big “Streaming” Data application are being written. It is rapidly adopted by companies spread across various business verticals – ad and social network monitoring, real-time analysis of machine data, fraud and anomaly detections, etc. These companies are mainly adopting Spark Streaming because – Its simple, declarative batch-like API makes large-scale stream processing accessible to non-scientists. – Its unified API and a single processing engine (i.e. Spark core engine) allows a single cluster and a single set of operational processes to cover the full spectrum of uses cases – batch, interactive and stream processing. – Its stronger, exactly-once semantics makes it easier to express and debug complex business logic. In this talk, I am going to elaborate on such adoption stories, highlighting interesting use cases of Spark Streaming in the wild. In addition, this presentation will also showcase the exciting new developments in Spark Streaming and the potential future roadmap.
Large scale, interactive ad-hoc queries over different datastores with Apache...jaxLondonConference
Presented at JAX London 2013
Apache Drill is a distributed system for interactive ad-hoc query and analysis of large-scale datasets. It is the Open Source version of Google’s Dremel technology. Apache Drill is designed to scale to thousands of servers and able to process Petabytes of data in seconds, enabling SQL-on-Hadoop and supporting a variety of data sources.
Spark Application Carousel: Highlights of Several Applications Built with SparkDatabricks
This talk from 2015 Spark Summit East covers 3 applications built with Apache Spark:
1. Web Logs Analysis: Basic Data Pipeline - Spark & Spark SQL
2. Wikipedia Dataset Analysis: Machine Learning
3. Facebook API: Graph Algorithms
Unified Big Data Processing with Apache Spark (QCON 2014)Databricks
While early big data systems, such as MapReduce, focused on batch processing, the demands on these systems have quickly grown. Users quickly needed to run (1) more interactive ad-hoc queries, (2) sophisticated multi-pass algorithms (e.g. machine learning), and (3) real-time stream processing. The result has been an explosion of specialized systems to tackle these new workloads. Unfortunately, this means more systems to learn, manage, and stitch together into pipelines. Spark is unique in taking a step back and trying to provide a *unified* post-MapReduce programming model that tackles all these workloads. By generalizing MapReduce to support fast data sharing and low-latency jobs, we achieve best-in-class performance in a variety of workloads, while providing a simple programming model that lets users easily and efficiently combine them.
Today, Spark is the most active open source project in big data, with high activity in both the core engine and a growing array of standard libraries built on top (e.g. machine learning, stream processing, SQL). I'm going to talk about the latest developments in Spark and show examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code.
Talk by Databricks CTO and Apache Spark creator Matei Zaharia at QCON San Francisco 2014.
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
At improve digital we collect and store large volumes of machine generated and behavioural data from our fleet of ad servers. For some time we have performed mostly batch processing through a data warehouse that combines traditional RDBMs (MySQL), columnar stores (Infobright, impala+parquet) and Hadoop.
We wish to share our experiences in enhancing this capability with systems and techniques that process the data as streams in near-realtime. In particular we will cover:
• The architectural need for an approach to data collection and distribution as a first-class capability
• The different needs of the ingest pipeline required by streamed realtime data, the challenges faced in building these pipelines and how they forced us to start thinking about the concept of production-ready data.
• The tools we used, in particular Apache Kafka as the message broker, Apache Samza for stream processing and Apache Avro to allow schema evolution; an essential element to handle data whose formats will change over time.
• The unexpected capabilities enabled by this approach, including the value in using realtime alerting as a strong adjunct to data validation and testing.
• What this has meant for our approach to analytics and how we are moving to online learning and realtime simulation.
This is still a work in progress at Improve Digital with differing levels of production-deployed capability across the topics above. We feel our experiences can help inform others embarking on a similar journey and hopefully allow them to learn from our initiative in this space.
Change Data Capture to Data Lakes Using Apache Pulsar and Apache Hudi - Pulsa...StreamNative
Apache Hudi is an open data lake platform, designed around the streaming data model. At its core, Hudi provides a transactions, upserts, deletes on data lake storage, while also enabling CDC capabilities. Hudi also provides a coherent set of table services, which can clean, compact, cluster and optimize storage layout for better query performance. Finally, Hudi's data services provide out-of-box support for streaming data from event systems into lake storage in near real-time.
In this talk, we will walk through an end-end use case for change data capture from a relational database, starting with capture changes using the Pulsar CDC connector and then demonstrate how you can use the Hudi deltastreamer tool to then apply these changes into a table on the data lake. We will discuss various tips to operationalizing and monitoring such pipelines. We will conclude with some guidance on future integrations between the two projects including a native Hudi/Pulsar connector and Hudi tiered storage.
This report describes how the Aucfanlab team used Azure’s Data Factory service to
implement the orchestration and monitoring of all data pipelines for our “Aucfan
Datalake” project.
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Amazon Web Services
Learn how to deploy a managed Presto environment to interactively query log data on AWS
Organizations often need to quickly analyze large amounts of data, such as logs, generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes
In this webinar you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using plain ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
Learning Objectives:
• Learn how to deploy a managed Presto environment running on Amazon EMR
• Understand best practices for running Presto on Amazon EMR, including use of Amazon EC2 Spot instances
• Learn how other customers are using Presto to analyze large data sets
(BDT303) Construct Your ETL Pipeline with AWS Data Pipeline, Amazon EMR, and ...Amazon Web Services
An advantage to leveraging Amazon Web Services for your data processing and warehousing use cases is the number of services available to construct complex, automated architectures easily. Using AWS Data Pipeline, Amazon EMR, and Amazon Redshift, we show you how to build a fault-tolerant, highly available, and highly scalable ETL pipeline and data warehouse. Coursera will show how they built their pipeline, and share best practices from their architecture.
Business intelligence requirements are changing and business users are moving more and more from historical reporting into predictive analytics in an attempt to get both a better and deeper understanding of their data. Traditionally, building an analytical platform has required an expensive infrastructure and a considerable amount of time for setup and deployment. Here we look at a quick and simple alternative.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
3. What I’ll Cover Tonight...
Basic intro and history of pipelines
Some examples of pipelines
An overview of big data pipelines
Some AWS technologies for building pipelines
4. History of pipelines
Invented by Douglas Mcllroy
Pipes added to UNIX in 1973
ps -ef | grep java
Processes chained together by their standard
streams
5. Pipeline concept brought into software
development mainstream
First used this for a message extractor,
analyser and indexing solution circa 2009
Enterprise integration patterns went further
Pipes and Filters Architecture
6. Task B Task CTask A
Task B Task CTask A
Pipeline A
Pipeline B
Pipes and Filters Architecture
7. Why? What do we achieve?
Decoupling of tasks
Encapsulation of processing within a task
Reuse of tasks in different workflows possibly
8. Some Considerations
How do we specify a task?
How do we feed data between the tasks?
When do they run, how often?
Do they run in serial or parallel?
What happens in terms of a step failure?
14. Nice, but generally found that:
People don’t like/want to work graphically with
pipelines, especially programmers
Still suitable for non-programmers who just want to
reuse past work though
Graphical Pipeline Summary
15. There’s some great lightweight solutions for
building pipelines in various languages:
Luigi (Python)
Piecepipe (Ruby)
Spring Integration and Batch (Java)
Lightweight Pipeline Solutions
16. Seeing as this is Bristol Java Meetup…
there’s a number of (non hadoop) Java data
pipeline frameworks including:
NorthConcepts Data Pipeline
Java Data Processing Framework
Spring Batch (and Spring Integration)
Java Data Pipelines
17. Set of patterns describing generic integration
patterns
Book By Gregor Hophe
Generally configure using XML
or a DSL of sorts
Enterprise Integration Patterns
19. Overview of EIP Patterns (Brief)
Message
Channel
Content
Enricher
20.
21. Another way to define a pipeline is as a
workflow with several connected steps
Store intermediate state in a database
JBPM
Activiti
Execute processes written in BPEL or BPMN
Workflow Engines
23. Luigi (Python)
Luigi is used internally at Spotify to build
complex data pipelines
Supports Hadoop, Spark for data processing
Supports Hive, HDFS and a range of other
data sinks
24. class AggregateArtists(luigi.Task):
date_interval = luigi.DateIntervalParameter()
def run(self):
artist_count = defaultdict(int)
for input in self.input():
with input.open('r') as in_file:
for line in in_file:
timestamp, artist, track = line.strip().split()
artist_count[artist] += 1
with self.output().open('w') as out_file:
for artist, count in artist_count.iteritems():
print >> out_file, artist, count
Programmatic Example
25.
26. Made up of a set of steps (contractive,
iterating, transforming)
Assembly Steps use hashes as their inputs
Piecepipe (Ruby)
30. Spring Batch
More suited to batch processing (as per the
name)
Specify the pipeline in XML or a DSL:
Job
Step, tasklets and chunks
Chunks refer to Spring beans
33. Write your Own?
Simplistic set of database tables, abstract
Step, Job and Workflow concepts
Link to scripts in Groovy/Clojure/JRuby for
dynamic
Store state in the filesystem, database or pass
hashmaps between the steps
36. Everybody loves Big Data right?
There he is the
smiley bastard...
Big Data Pipelines
37. Pipelines we’ve seen so far
Generally serial - no good when we need to
shift large amounts of data around.
Better option is to parallelise the processing
In some of the cases before (e.g. Luigi) you
can shift processing to Hadoop, Storm etc...
38. Parallelising the data
Java works well here with a great concurrency
API, executor services, locks etc…
Cumbersome to write, we have to partition the
data correctly to begin with
Spring integration/batch helps with abstractions
39. Hadoop - map reduce
Hadoop is a good solution for shifting big data
Batch processing though - job startup time can
take minutes
Also it’s pretty cumbersome to write Hadoop
mappers/reducers in Java
40. Pig and Oozie
Fortunately we have some help
Pig is a SQL like language aimed at making
MapReduce jobs easier to write
Oozie provides a way of building graphical
pipelines using Hadoop
41. Word Count - yada yada
a = load 'words.txt';
b = foreach a generate flatten(TOKENIZE((chararray)$0)) as
word;
c = group b by word;
d = foreach c generate COUNT(b), group;
store d into '/user/jon';
44. Big Data Vendors
Lots of solutions for generalising data
processing into a platform solution
Hadoop - batch processing
Storm - real-time interactive processing
Similar to the graphical pipelines we saw
earlier on
48. Apache Falcon - Data Processing
Apache Incubator project, 0.6 currently
Think of it as an open-source data
management platform
Late data handling, retry policies etc...
Colo and global aggregations
50. Amazon Web Service Components
We’re big users of AWS and components:
AWS RDS - main datastore
AWS Datapipe - basic ETL processes (log processing)
AWS DynamoDB - dead code analysis
AWS S3 - Log file source
Also use BigQuery to store large amounts of
parsed access logs
51. Amazon Web Service Components
AWS provides a number of tools for building
data pipelines
Some are packaged open-source tools
Useful when you don’t want to setup your own
infrastructure.
Not necessarily cheaper though
52. Google Pipeline Components
Google also has a set of components for
building data pipelines:
Cloud Dataflow - (AWS Data Pipeline)
Google Cloud Storage (AWS S3)
BigQuery - (AWS Redshift)
App Engine Pipeline API
53. Lambda
Trigger actions based
on events
Elastic Map Reduce
Parallel Data
Processing
Data Pipeline
Extract, Transform,
Load Data
Kinesis
Realtime Data
Stream Processing
S3
RDS
Redshift
Data Sources Data Processing Data Sinks
S3 RDS Redshift
Elastic
Beanstalk
Dashboards
Elastic
Beanstalk
Dashboards
55. Hadoopken...
Hadoop distro is Amazon’s own
Allows you to create a cluster with n nodes
Logs directly to S3
Security/Access through standard IAM
Elastic Map Reduce
56. Choice of applications to install in addition:
Various databases (Hive, HBase or Impala)
Pig - SQL like data processing
Hunk - Splunk analytics for Hadoop
Ganglia - for monitoring the cluster
Elastic Map Reduce
57. Bootstrap actions before Hadoop starts
Submit unit of work to the cluster:
Streaming program
Hive
Pig - can do streaming with this as well...
Impala
Custom JAR
Elastic Map Reduce
59. Data Pipeline
Data Node - the source of our data
Amazon S3 Filesystem
MySQL, Redshift, Dynamo, SQL Data Node
Activity - processor
Shell Commands
Copy Command
EMR, Pig, Hive
61. Define a datasource - S3 Logs
Runs every day
Can specify:
Attempts
Failure Actions
Success Actions/Follow up jobs
Data Pipeline - Source
62. Define a datasource - S3 Logs
Runs every day
Can specify:
Attempts
Failure Actions
Success Actions/Follow up jobs
Data Pipeline - Source
63. Simple processor that specifies a Ruby script to
run to:
Read in files from S3
Parse the access log lines with universal-
access-log-parser (awesome)
Insert the results into a BigQuery table
Data Pipeline - Processor
64. Elastic Map Reduce
Parallel Data
Processing
Data Pipeline
Extract, Transform,
Load Data
S3
Data Sources Data Processing Data Sinks
Google
BigQuery
Ruby + Sinatra
Highcharts
EC2 Servers
PHP UI
Java Services
Ruby
66. AWS Kinesis (Real Time Streaming)
Simple set of steps:
Ingestion of data (Producer)
Durable storage of data
Consumers do parallel processing
Only keeps 24 hours of data
67. Sending Data (PutRecordsRequest)
PutRecordsRequest req = new PutRecordsRequest();
req.setStreamName(myStreamName);
List<PutRecordsRequestEntry> entryList = new ArrayList<>();
for (int j = 0; j < 100; j++) {
PutRecordsRequestEntry entry = new PutRecordsRequestEntry();
entry.setData(ByteBuffer.wrap(String.valueOf(i).getBytes()));
entry.setPartitionKey(String.format("partitionKey-%d", i));
entryList.add(entry);
}
PutRecordsResult res = kinesis.putRecords(req);
68. Consuming Data (GetRecords)
Need to use the Kinesis Client Library
Implement IRecordProcessor class and
implement init, processRecords and
shutdown
Call checkpoint when done
https://github.com/awslabs/amazon-kinesis-client
69. Scaling this out
Number of ways:
Assign more shards to a stream
Increase the EC2 instance size
Increase EC2 instances up to max no. shards
Shards are stored in DynamoDB
Can also use auto scaling to modify the
number of shards
70. Utilising Kinesis
Alternative to parsing your logs for various
metrics - these might be a day old
Real-time metrics information from various
sources:
Rate of orders being processed
Web application click stream data
71. Runs code in response to events
Data Events
Scheduled Events
External Events (sensors, alarms etc…)
Provision EC2 instances or AWS Containers
Auto scaling
AWS Lambda (Preview currently)
72. Sysadmin/Devops
AWS (S3, EC2, Dynamo, Data Pipeline) Ruby and Chef hackery
Senior/Mid Java Developers
Elasticsearch, RabbitMQ, Hazelcast, Spring
Solutions Architect
eCommerce experience
www.brightpearl.com/careers
Questions? Btw… we’re recruiting