The analytics platform at Twitter has experienced tremendous growth over the past few years in terms of size, complexity, number of users, and variety of use cases. In this talk, we’ll discuss the evolution of our infrastructure and the development of capabilities for data mining on “big data”. We’ll share our experiences as a case study, but make recommendations for best practices and point out opportunities for future work.
We present our solution for building an AI Architecture that provides engineering teams the ability to leverage data to drive insight and help our customers solve their problems. We started with siloed data, entities that were described differently by each product, different formats, complicated security and access schemes, data spread over numerous locations and systems.
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Cloud Cost Management and Apache Spark with Xuan WangDatabricks
The cloud computing market is growing faster than virtually any other IT market today, according to Gartner [1]. Providing a unified analytics platform in public clouds, Databricks invests heavily in cloud computing. As a result, cloud expense becomes an imperative category of our cost of goods sold (COGS) and operating expense (OPEX). Many companies share the same story as ours, embracing the cloud while facing the raising challenge of managing its cost.
In this session, we will share our experience on cloud cost management, from mistakes we made, data garnered, lessons learned, to the solutions we built. We will discuss general principles of managing accounts and services and assigning budget and attributing cost to internal teams. Using AWS as a concrete example, with Databricks and Spark as part of our solution, we will show how we: 1) make AWS cost and usage data available to finance and budget owners, 2) build data products that help budget owners to monitor the cost and take actions by buying reserved instances and setting retention policies, 3) use data science techniques to detect changes and do forecast. The general principles and solutions we built are applicable to other cloud providers too.
Cost Efficiency Strategies for Managed Apache Spark ServiceDatabricks
Today with cloud-native rising, the conversation of infrastructure costs seeped from R&D Directors to every person in the R&D: “How much a VM costs?”, “can we use that managed services? How much will it cost us with our workload??” , “I need a stronger machine with more GPU, how do we make it happen within the budget?” sounds familiar?
We present our solution for building an AI Architecture that provides engineering teams the ability to leverage data to drive insight and help our customers solve their problems. We started with siloed data, entities that were described differently by each product, different formats, complicated security and access schemes, data spread over numerous locations and systems.
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Cloud Cost Management and Apache Spark with Xuan WangDatabricks
The cloud computing market is growing faster than virtually any other IT market today, according to Gartner [1]. Providing a unified analytics platform in public clouds, Databricks invests heavily in cloud computing. As a result, cloud expense becomes an imperative category of our cost of goods sold (COGS) and operating expense (OPEX). Many companies share the same story as ours, embracing the cloud while facing the raising challenge of managing its cost.
In this session, we will share our experience on cloud cost management, from mistakes we made, data garnered, lessons learned, to the solutions we built. We will discuss general principles of managing accounts and services and assigning budget and attributing cost to internal teams. Using AWS as a concrete example, with Databricks and Spark as part of our solution, we will show how we: 1) make AWS cost and usage data available to finance and budget owners, 2) build data products that help budget owners to monitor the cost and take actions by buying reserved instances and setting retention policies, 3) use data science techniques to detect changes and do forecast. The general principles and solutions we built are applicable to other cloud providers too.
Cost Efficiency Strategies for Managed Apache Spark ServiceDatabricks
Today with cloud-native rising, the conversation of infrastructure costs seeped from R&D Directors to every person in the R&D: “How much a VM costs?”, “can we use that managed services? How much will it cost us with our workload??” , “I need a stronger machine with more GPU, how do we make it happen within the budget?” sounds familiar?
Build Real-Time Applications with Databricks StreamingDatabricks
In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Wizard Driven AI Anomaly Detection with Databricks in AzureDatabricks
Fraud is prevalent in every industry, and growing at an increasing rate, as the volume of transactions increases with automation. The National Healthcare Anti-Fraud Association estimates $350B of fraudulent spending. Forbes estimates $25B spending by US banks on anti-money laundering compliance. At the same time as fraud and anomaly detection use cases are booming, the skills gap of expert data scientists available to perform fraud detection is widening.
The Kavi Global team will present a cloud native, wizard-driven AI anomaly detection solution, enabling Citizen Data Scientists to easily create anomaly detection models to automatically flag Collective, Contextual, and Point anomalies, at the transaction level, as well as collusion between actors. Unsupervised methods (Distribution, Clustering, Association, Sequencing, Historical Occurrence, Custom Rules) and supervised (Random Forest, Neural Network) models are executed in Apache Spark on Databricks.
An innovative aggregation framework converts probabilistic fraud scores and their probabilities into a meaningful and actionable prioritized list of suspicious (a statistical outlier) and potentially fraudulent transaction to be investigated from a business point of view. The AI Anomaly Detection models improve over time using Human-in-the-Loop feedback methods to label data for supervised modeling.
Finally, The Kavi team overviews the Anomaly Lifecycle: from statistical outlier to validated business fraud for reclaim and business process changes to long term prevention strategies using proactive audits upstream at the time of estimate to prevent revenue leakage. Two client success stories will be presented acros Pharmaceutical Rx and Transportation industries.
Mixing Analytic Workloads with Greenplum and Apache SparkVMware Tanzu
Apache Spark is a popular in-memory data analytics engine because of its speed, scalability, and ease of use. It also fits well with DevOps practices and cloud-native software platforms. It’s good for data exploration, interactive analytics, and streaming use cases.
However, Spark, like other data-processing platforms, is not one size fits all. Different versions of Spark support different feature sets, and Spark’s machine-learning libraries can also vary in important ways between versions, or may lack the right algorithm.
In this webinar, you’ll learn:
- How to integrate data warehouse workloads with Spark
- Which workloads are better for Greenplum and for Spark
- How to use the Greenplum-Spark connector
Presenter: Kong Yew Chan, Product Manager, Pivotal
Making Apache Spark Better with Delta LakeDatabricks
Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
In this talk, we will cover:
* What data quality problems Delta helps address
* How to convert your existing application to Delta Lake
* How the Delta Lake transaction protocol works internally
* The Delta Lake roadmap for the next few releases
* How to get involved!
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Cloud dw benchmark using tpd-ds( Snowflake vs Redshift vs EMR Hive )SANG WON PARK
몇년 전부터 Data Architecture의 변화가 빠르게 진행되고 있고,
그 중 Cloud DW는 기존 Data Lake(Hadoop 기반)의 한계(성능, 비용, 운영 등)에 대한 대안으로 주목받으며,
많은 기업들이 이미 도입했거나, 도입을 검토하고 있다.
본 자료는 이러한 Cloud DW에 대해서 개념적으로 이해하고,
시장에 존재하는 다양한 Cloud DW 중에서 기업의 환경에 맞는 제품이 어떤 것인지 성능/비용 관점으로 비교했다.
- 왜기업들은 CloudDW에주목하는가?
- 시장에는어떤 제품들이 있는가?
- 우리Biz환경에서는 어떤 제품을 도입해야 하는가?
- CloudDW솔루션의 성능은?
- 기존DataLake(EMR)대비 성능은?
- 유사CloudDW(snowflake vs redshift) 대비성능은?
앞으로도 Data를 둘러싼 시장은 Cloud DW를 기반으로 ELT, Mata Mesh, Reverse ETL등 새로운 생테계가 급속하게 발전할 것이고,
이를 위한 데이터 엔지니어/데이터 아키텍트 관점의 기술적 검토와 고민이 필요할 것 같다.
https://blog.naver.com/freepsw/222654809552
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
The evolution of the big data platform @ Netflix (OSCON 2015)Eva Tse
At Netflix, the big data platform is the foundation for analytics that drive all product decisions that directly impact our customer experience. As for scale, it is one of the top three largest services running at Netflix, in terms of compute power and data size.
In this talk, we will take the audience through a journey to understand how we scale the platform to handle the increasing amount of data (over 500 billion events generated daily), the increasing demand of analytics (which translates to compute power), and the increasing number of users dependent on our platform to make business decisions.
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/
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
"Conceptually, a data lake is a flat data store to collect data in its original form, without the need to enforce a predefined schema. Instead, new schemas or views are created “on demand”, providing a far more agile and flexible architecture while enabling new types of analytical insights. AWS provides many of the building blocks required to help organizations implement a data lake. In this session, we will introduce key concepts for a data lake and present aspects related to its implementation. We will discuss critical success factors, pitfalls to avoid as well as operational aspects such as security, governance, search, indexing and metadata management. We will also provide insight on how AWS enables a data lake architecture.
A data lake is a flat data store to collect data in its original form, without the need to enforce a predefined schema. Instead, new schemas or views are created ""on demand"", providing a far more agile and flexible architecture while enabling new types of analytical insights. AWS provides many of the building blocks required to help organizations implement a data lake. In this session, we introduce key concepts for a data lake and present aspects related to its implementation. We discuss critical success factors and pitfalls to avoid, as well as operational aspects such as security, governance, search, indexing, and metadata management. We also provide insight on how AWS enables a data lake architecture. Attendees get practical tips and recommendations to get started with their data lake implementations on AWS."
An architecture for federated data discovery and lineage over on-prem datasou...DataWorks Summit
Comcast's Streaming Data platform comprises a variety of ingest, transformation, and storage services in the public cloud. Peer-reviewed Apache Avro schemas support end-to-end data governance. We have previously reported (DataWorks Summit 2017) on how we extended Atlas with custom entity and process types for discovery and lineage in the AWS public cloud. Custom lambda functions notify Atlas of creation of new entities and new lineage links via asynchronous kafka messaging.
Recently we were presented the challenge of providing integrated data discovery and lineage across our public cloud datasources and on-prem datasources, both Hadoop-based and traditional data warehouses and RDBMSs. Can Apache Atlas meet this challenge? A resounding yes! This talk will present our federated architecture, with Atlas providing SQL-like, free-text, and graph search across select metadata from all on-prem and public cloud data sources in our purview. Lightweight, custom connectors/bridges identify metadata/lineage changes in underlying sources and publish them to Atlas via the asynchronous API. A portal layer provides Atlas query access and a federation of UIs. Once data of interest is identified via Atlas queries, interfaces specific to underlying sources may be used for special-purpose metadata mining.
While metadata repositories for data discovery and lineage abound, none of them have built-in connectors and listeners for the entire complement of data sources that Comcast and many other large enterprises use to support their business needs. In-house-built solutions typically underestimate the cost of development and maintenance and often suffer from architecture-by-accretion. Atlas' commitment to extensibility, built-in provision of typed, free-text, and graph search, and REST and asynchronous APIs, position it uniquely in the build-vs-buy sweet spot.
Observability for Data Pipelines With OpenLineageDatabricks
Data is increasingly becoming core to many products. Whether to provide recommendations for users, getting insights on how they use the product, or using machine learning to improve the experience. This creates a critical need for reliable data operations and understanding how data is flowing through our systems. Data pipelines must be auditable, reliable, and run on time. This proves particularly difficult in a constantly changing, fast-paced environment.
Collecting this lineage metadata as data pipelines are running provides an understanding of dependencies between many teams consuming and producing data and how constant changes impact them. It is the underlying foundation that enables the many use cases related to data operations. The OpenLineage project is an API standardizing this metadata across the ecosystem, reducing complexity and duplicate work in collecting lineage information. It enables many projects, consumers of lineage in the ecosystem whether they focus on operations, governance or security.
Marquez is an open source project part of the LF AI & Data foundation which instruments data pipelines to collect lineage and metadata and enable those use cases. It implements the OpenLineage API and provides context by making visible dependencies across organizations and technologies as they change over time.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
Build Real-Time Applications with Databricks StreamingDatabricks
In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Wizard Driven AI Anomaly Detection with Databricks in AzureDatabricks
Fraud is prevalent in every industry, and growing at an increasing rate, as the volume of transactions increases with automation. The National Healthcare Anti-Fraud Association estimates $350B of fraudulent spending. Forbes estimates $25B spending by US banks on anti-money laundering compliance. At the same time as fraud and anomaly detection use cases are booming, the skills gap of expert data scientists available to perform fraud detection is widening.
The Kavi Global team will present a cloud native, wizard-driven AI anomaly detection solution, enabling Citizen Data Scientists to easily create anomaly detection models to automatically flag Collective, Contextual, and Point anomalies, at the transaction level, as well as collusion between actors. Unsupervised methods (Distribution, Clustering, Association, Sequencing, Historical Occurrence, Custom Rules) and supervised (Random Forest, Neural Network) models are executed in Apache Spark on Databricks.
An innovative aggregation framework converts probabilistic fraud scores and their probabilities into a meaningful and actionable prioritized list of suspicious (a statistical outlier) and potentially fraudulent transaction to be investigated from a business point of view. The AI Anomaly Detection models improve over time using Human-in-the-Loop feedback methods to label data for supervised modeling.
Finally, The Kavi team overviews the Anomaly Lifecycle: from statistical outlier to validated business fraud for reclaim and business process changes to long term prevention strategies using proactive audits upstream at the time of estimate to prevent revenue leakage. Two client success stories will be presented acros Pharmaceutical Rx and Transportation industries.
Mixing Analytic Workloads with Greenplum and Apache SparkVMware Tanzu
Apache Spark is a popular in-memory data analytics engine because of its speed, scalability, and ease of use. It also fits well with DevOps practices and cloud-native software platforms. It’s good for data exploration, interactive analytics, and streaming use cases.
However, Spark, like other data-processing platforms, is not one size fits all. Different versions of Spark support different feature sets, and Spark’s machine-learning libraries can also vary in important ways between versions, or may lack the right algorithm.
In this webinar, you’ll learn:
- How to integrate data warehouse workloads with Spark
- Which workloads are better for Greenplum and for Spark
- How to use the Greenplum-Spark connector
Presenter: Kong Yew Chan, Product Manager, Pivotal
Making Apache Spark Better with Delta LakeDatabricks
Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
In this talk, we will cover:
* What data quality problems Delta helps address
* How to convert your existing application to Delta Lake
* How the Delta Lake transaction protocol works internally
* The Delta Lake roadmap for the next few releases
* How to get involved!
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Cloud dw benchmark using tpd-ds( Snowflake vs Redshift vs EMR Hive )SANG WON PARK
몇년 전부터 Data Architecture의 변화가 빠르게 진행되고 있고,
그 중 Cloud DW는 기존 Data Lake(Hadoop 기반)의 한계(성능, 비용, 운영 등)에 대한 대안으로 주목받으며,
많은 기업들이 이미 도입했거나, 도입을 검토하고 있다.
본 자료는 이러한 Cloud DW에 대해서 개념적으로 이해하고,
시장에 존재하는 다양한 Cloud DW 중에서 기업의 환경에 맞는 제품이 어떤 것인지 성능/비용 관점으로 비교했다.
- 왜기업들은 CloudDW에주목하는가?
- 시장에는어떤 제품들이 있는가?
- 우리Biz환경에서는 어떤 제품을 도입해야 하는가?
- CloudDW솔루션의 성능은?
- 기존DataLake(EMR)대비 성능은?
- 유사CloudDW(snowflake vs redshift) 대비성능은?
앞으로도 Data를 둘러싼 시장은 Cloud DW를 기반으로 ELT, Mata Mesh, Reverse ETL등 새로운 생테계가 급속하게 발전할 것이고,
이를 위한 데이터 엔지니어/데이터 아키텍트 관점의 기술적 검토와 고민이 필요할 것 같다.
https://blog.naver.com/freepsw/222654809552
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
The evolution of the big data platform @ Netflix (OSCON 2015)Eva Tse
At Netflix, the big data platform is the foundation for analytics that drive all product decisions that directly impact our customer experience. As for scale, it is one of the top three largest services running at Netflix, in terms of compute power and data size.
In this talk, we will take the audience through a journey to understand how we scale the platform to handle the increasing amount of data (over 500 billion events generated daily), the increasing demand of analytics (which translates to compute power), and the increasing number of users dependent on our platform to make business decisions.
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/
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
"Conceptually, a data lake is a flat data store to collect data in its original form, without the need to enforce a predefined schema. Instead, new schemas or views are created “on demand”, providing a far more agile and flexible architecture while enabling new types of analytical insights. AWS provides many of the building blocks required to help organizations implement a data lake. In this session, we will introduce key concepts for a data lake and present aspects related to its implementation. We will discuss critical success factors, pitfalls to avoid as well as operational aspects such as security, governance, search, indexing and metadata management. We will also provide insight on how AWS enables a data lake architecture.
A data lake is a flat data store to collect data in its original form, without the need to enforce a predefined schema. Instead, new schemas or views are created ""on demand"", providing a far more agile and flexible architecture while enabling new types of analytical insights. AWS provides many of the building blocks required to help organizations implement a data lake. In this session, we introduce key concepts for a data lake and present aspects related to its implementation. We discuss critical success factors and pitfalls to avoid, as well as operational aspects such as security, governance, search, indexing, and metadata management. We also provide insight on how AWS enables a data lake architecture. Attendees get practical tips and recommendations to get started with their data lake implementations on AWS."
An architecture for federated data discovery and lineage over on-prem datasou...DataWorks Summit
Comcast's Streaming Data platform comprises a variety of ingest, transformation, and storage services in the public cloud. Peer-reviewed Apache Avro schemas support end-to-end data governance. We have previously reported (DataWorks Summit 2017) on how we extended Atlas with custom entity and process types for discovery and lineage in the AWS public cloud. Custom lambda functions notify Atlas of creation of new entities and new lineage links via asynchronous kafka messaging.
Recently we were presented the challenge of providing integrated data discovery and lineage across our public cloud datasources and on-prem datasources, both Hadoop-based and traditional data warehouses and RDBMSs. Can Apache Atlas meet this challenge? A resounding yes! This talk will present our federated architecture, with Atlas providing SQL-like, free-text, and graph search across select metadata from all on-prem and public cloud data sources in our purview. Lightweight, custom connectors/bridges identify metadata/lineage changes in underlying sources and publish them to Atlas via the asynchronous API. A portal layer provides Atlas query access and a federation of UIs. Once data of interest is identified via Atlas queries, interfaces specific to underlying sources may be used for special-purpose metadata mining.
While metadata repositories for data discovery and lineage abound, none of them have built-in connectors and listeners for the entire complement of data sources that Comcast and many other large enterprises use to support their business needs. In-house-built solutions typically underestimate the cost of development and maintenance and often suffer from architecture-by-accretion. Atlas' commitment to extensibility, built-in provision of typed, free-text, and graph search, and REST and asynchronous APIs, position it uniquely in the build-vs-buy sweet spot.
Observability for Data Pipelines With OpenLineageDatabricks
Data is increasingly becoming core to many products. Whether to provide recommendations for users, getting insights on how they use the product, or using machine learning to improve the experience. This creates a critical need for reliable data operations and understanding how data is flowing through our systems. Data pipelines must be auditable, reliable, and run on time. This proves particularly difficult in a constantly changing, fast-paced environment.
Collecting this lineage metadata as data pipelines are running provides an understanding of dependencies between many teams consuming and producing data and how constant changes impact them. It is the underlying foundation that enables the many use cases related to data operations. The OpenLineage project is an API standardizing this metadata across the ecosystem, reducing complexity and duplicate work in collecting lineage information. It enables many projects, consumers of lineage in the ecosystem whether they focus on operations, governance or security.
Marquez is an open source project part of the LF AI & Data foundation which instruments data pipelines to collect lineage and metadata and enable those use cases. It implements the OpenLineage API and provides context by making visible dependencies across organizations and technologies as they change over time.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
Apache Hadoop YARN: Understanding the Data Operating System of HadoopHortonworks
This deck covers concepts and motivations behind Apache Hadoop YARN, the key technology in Hadoop 2 to deliver a Data Operating System for the enterprise.
EclipseCon Keynote: Apache Hadoop - An IntroductionCloudera, Inc.
Todd Lipcon explains why you should be interested in Apache Hadoop, what it is, and how it works. Todd also brings to light the Hadoop ecosystem and real business use cases that evolve around Hadoop and the ecosystem.
The Fundamentals Guide to HDP and HDInsightGert Drapers
This session will give you the architectural overview and introduction in to inner workings of HDP 2.0 (http://hortonworks.com/products/hdp-windows/) and HDInsight. The world has embraced the Hadoop toolkit to solve their data problems from ETL, data warehouses to event processing pipelines. As Hadoop consists of many components, services and interfaces, understanding its architecture is crucial, before you can successfully integrate it in to your own environment.
Spark and Shark: Lightning-Fast Analytics over Hadoop and Hive DataJetlore
Spark is an open source cluster computing framework that can outperform Hadoop by 30x through a combination of in-memory computation and a richer execution engine. Shark is a port of Apache Hive onto Spark, which provides a similar speedup for SQL queries, allowing interactive exploration of data in existing Hive warehouses. This talk will cover how both Spark and Shark are being used at various companies to accelerate big data analytics, the architecture of the systems, and where they are heading. We will also discuss the next major feature we are developing, Spark Streaming, which adds support for low-latency stream processing to Spark, giving users a unified interface for batch and real-time analytics.
FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...Zhenzhong Xu
Netflix is obsessed with customer joy, we relentlessly focus on product experience and high-quality content. In recent years, we have been making heavy investments in the tech-driven studio and content production. As a result, a lot of unique challenges arise in the real-time data infrastructure space. For example, in a microservices architecture, domain entities are spread in different applications and persistence storages, this made low latency consistent operational reporting and entity searching especially challenging.
In this talk, we’ll talk about some interesting use cases, the various challenges lay in the fundamentals of distributed systems, and how did we solve them. We will also discuss the learnings, things we could’ve done differently, and the new vision towards an open self-serving Data Mesh platform that empowers our partners and users to build flexible real-time data pipelines.
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Data serves as the platform for decision-making at Uber. To facilitate data driven decisions, many datasets at Uber are ingested in a Hadoop Data Lake and exposed to querying via Hive. Analytical queries joining various datasets are run to better understand business data at Uber.
Data ingestion, at its most basic form, is about organizing data to balance efficient reading and writing of newer data. Data organization for efficient reading involves factoring in query patterns to partition data to ensure read amplification is low. Data organization for efficient writing involves factoring the nature of input data - whether it is append only or updatable.
At Uber we ingest terabytes of many critical tables such as trips that are updatable. These tables are fundamental part of Uber's data-driven solutions, and act as the source-of-truth for all the analytical use-cases across the entire company. Datasets such as trips constantly receive updates to the data apart from inserts. To ingest such datasets we need a critical component that is responsible for bookkeeping information of the data layout, and annotates each incoming change with the location in HDFS where this data should be written. This component is called as Global Indexing. Without this component, all records get treated as inserts and get re-written to HDFS instead of being updated. This leads to duplication of data, breaking data correctness and user queries. This component is key to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. This component will need to have strong consistency and provide large throughputs for index writes and reads.
At Uber, we have chosen HBase to be the backing store for the Global Indexing component and is a critical component in allowing us to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. In this talk, we will discuss data@Uber and expound more on why we built the global index using Apache Hbase and how this helps to scale out our cluster usage. We’ll give details on why we chose HBase over other storage systems, how and why we came up with a creative solution to automatically load Hfiles directly to the backend circumventing the normal write path when bootstrapping our ingestion tables to avoid QPS constraints, as well as other learnings we had bringing this system up in production at the scale of data that Uber encounters daily.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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/
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Scaling Big Data Mining Infrastructure Twitter Experience
1. Scaling Big Data
Mining Infrastructure
Jimmy Lin Dmitriy Ryaboy
@lintool @squarecog
Hadoop Summit Europe
Thursday, March 21, 2013
2. From the Ivory Tower…
Source: Wikipedia (All Souls College, Oxford)
3. … to building sh*t that works.
Source: Wikipedia (Factory)
4. IMHO
Represents personal opinion. Not official position of Twitter.
Management not responsible for misuse. Void where prohibited. YMMV.
(If someone asks, I probably wasn’t here)
5.
6. “Yesterday”
~150 people total
~60 Hadoop nodes
~6 people use analytics stack daily
7. “Today”
~1400 people total
10s of Ks of Hadoop nodes, multiple DCs
10s of PBs total Hadoop DW capacity
~100 TB ingest daily
dozens of teams use Hadoop daily
10s of Ks of Hadoop jobs daily
11. It’s impossible to overstress this: 80%
of the work in any data project is in
cleaning the data. – DJ Patil ―Data
Jujitsu‖
Source: Wikipedia (Jujitsu)
12. Reality
Your boss says something vague
You think very hard on how to move the needle
Where’s the data?
What’s in this dataset?
What’s all the f#$!* crap in the data?
Clean the data
Run some off-the-shelf data mining algorithm
…
Productionize, act on the insight
Rinse, repeat
13. Data science is less
glamorous that you think!
How do we make data scientists’ lives a bit easier?
24. JSON to the Rescue!
Source: http://www.flickr.com/photos/snukkel/3206405352/
25. This should really be a list…
Remember the camelSnake!
{
"token": 945842,
"feature_enabled": "super_special",
"userid": 229922,
"page": "null", Is this really an integer?
"info": { "email": "my@place.com" }
}
Is this really null?
What keys? What values?
This does not scale.
26. struct MessageInfo {
1: optional string name
2: optional string email
}
struct LogMessage {
1: required i64 token
2: required string user_id
3: optional list<Feature> enabled_features
4: optional i64 page = 0
5: optional MessageInfo info
}
+ DDL provides type safety
enum Feature {
super_special, + Auto codgen
less_special
} + Efficient serialization
+ Sane schema migration
+ Separate logical from
physical
Use Thrift. or Protobufs.or Avro.
27. Schemas aren’t enough!
We need a data discovery service!
Where’s the data?
How do I read it?
Who produces it?
Who consumes it?
When was it last generated?
…
28. Where to find data?
Old way:
Hard-coded partitioning scheme, path, format
A = LOAD ‘/tables/statuses/2011/01/{05,06,07}/*.lzo’
USING LzoStatusProtobufBlockPigLoader();
Custom loader
How do people know? 1.) Ask around 2.) Cargo-cult
New way:
Nice UI for browsing
A = LOAD ‘tables.statuses’
USING TwadoopLoader(); Same loader each time
B = FILTER A BY year == ‘2011’
AND month == ‘11’
AND day == ‘01’
AND hour >= ’05’
AND hour <= ‘07’;
Filters are pushed into the loader.
No need to understand partitioning
scheme.
29. Data Access Layer (DAL)
―All problems in computer science can be solved by another level
of indirection... Except for the problem of too many layers of
indirection.‖ – David Wheeler
All data accesses go through DAL
Thin layer on top of HCatalog
30. Data Access Layer (DAL)
Who wrote what data, when?
#win
Automatically construct data/job dependency graph
Automatically figure out ownership
Hooks into alerting, auditing, repos,
deploy systems, etc.
31. Plumbing
Jimmy Lin and Alek Kolcz. Large-Scale Machine Learning at Twitter.
SIGMOD 2012.
Source: Wikipedia (Plumbing)
35. upload results
data munging
Joining multiple dataset
Feature extraction
… download
down-sample test data
train predict
36. What doesn’t work…
1. Down-sampling for training on single-processor
Defeats the whole point of big data!
2. Ad hoc productionizing
Disconnected from rest of production workflow
37. Production considerations:
dependency management
scheduling
resource allocation
monitoring
error reporting
alerting
…
We need…
40. Stochastic Gradient Descent
Conceptually, classifier training is a like
user-defined aggregate function!
AVG SGD
initialize sum = 0 initialize
count = 0 weights
update add to sum
increment
count
terminate return sum / count return weights
41. previous Pig dataflow previous Pig dataflow
map
Classifier
Training
reduce
label, feature vector
Pig storage
function
model model model
feature vector feature vector
Making model UDF model UDF
Predictions prediction prediction
Just like any other parallel Pig dataflow
42. It’s just Pig!
For ―free‖: dependency management,
scheduling, resource allocation,
monitoring, error reporting, alerting, …
44. Takeaway messages
How do we make data scientists’ lives a bit easier?
Adding a bit of structure goes a long way
Getting the plumbing right makes all the difference
―In theory, there is no difference between
theory and practice. But, in practice, there
is.‖
Questions?
- Jan L.A. van de Snepscheut