A Collaborative Data Science Development WorkflowDatabricks
Collaborative data science workflows have several moving parts, and many organizations struggle with developing an efficient and scalable process. Our solution consists of data scientists individually building and testing Kedro pipelines and measuring performance using MLflow tracking. Once a strong solution is created, the candidate pipeline is trained on cloud-agnostic, GPU-enabled containers. If this pipeline is production worthy, the resulting model is served to a production application through MLflow.
10 Things Learned Releasing Databricks Enterprise WideDatabricks
Implementing tools, let alone an entire Unified Data Platform, like Databricks, can be quite the undertaking. Implementing a tool which you have not yet learned all the ins and outs of can be even more frustrating. Have you ever wished that you could take some of that uncertainty away? Four years ago, Western Governors University (WGU) took on the task of rewriting all of our ETL pipelines in Scala/Python, as well as migrating our Enterprise Data Warehouse into Delta, all on the Databricks platform. Starting with 4 users and rapidly growing to over 120 users across 8 business units, our Databricks environment turned into an entire unified platform, being used by individuals of all skill levels, data requirements, and internal security requirements.
Through this process, our team has had the chance and opportunity to learn while making a lot of mistakes. Taking a look back at those mistakes, there are a lot of things we wish we had known before opening the platform to our enterprise.
We would like to share with you 10 things we wish we had known before WGU started operating in our Databricks environment. Covering topics surrounding user management from both an AWS and Databricks perspective, understanding and managing costs, creating custom pipelines for efficient code management, learning about new Apache Spark snippets that helped save us a fortune, and more. We would like to provide our recommendations on how one can overcome these pitfalls to help new, current and prospective users to make their environments easier, safer, and more reliable to work in.
SQL Analytics Powering Telemetry Analysis at ComcastDatabricks
Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience.
In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses.
We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.
Empowering Zillow’s Developers with Self-Service ETLDatabricks
As the amount of data and the number of unique data sources within an organization grow, handling the volume of new pipeline requests becomes difficult. Not all new pipeline requests are created equal — some are for business-critical datasets, others are for routine data preparation, and others are for experimental transformations that allow data scientists to iterate quickly on their solutions.
To meet the growing demand for new data pipelines, Zillow created multiple self-service solutions that enable any team to build, maintain, and monitor their data pipelines. These tools abstract away the orchestration, deployment, and Apache Spark processing implementation from their respective users. In this talk, Zillow engineers discuss two internal platforms they created to address the specific needs of two distinct user groups: data analysts and data producers. Each platform addresses the use cases of its intended user, leverages internal services through its modular design, and empowers users to create their own ETL without having to worry about how the ETL is implemented.
Members of Zillow’s data engineering team discuss:
Why they created two separate user interfaces to meet the needs different user groups
What degree of abstraction from the orchestration, deployment, processing, and other ancillary tasks that chose for each user group
How they leveraged internal services and packages, including their Apache Spark package — Pipeler, to democratize the creation of high-quality, reliable pipelines within Zillow
How We Optimize Spark SQL Jobs With parallel and sync IODatabricks
Although NVMe has been more and more popular these years, a large amount of HDD are still widely used in super-large scale big data clusters. In a EB-level data platform, IO(including decompression and decode) cost contributes a large proportion of Spark jobs’ cost. In another word, IO operation is worth optimizing.
In ByteDancen, we do a series of IO optimization to improve performance, including parallel read and asynchronized shuffle. Firstly we implement file level parallel read to improve performance when there are a lot of small files. Secondly, we design row group level parallel read to accelerate queries for big-file scenario. Thirdly, implement asynchronized spill to improve job peformance. Besides, we design parquet column family, which will split a table into a few column families and different column family will be in different Parquets files. Different column family can be read in parallel, so the read performance is much higher than the existing approach. In our practice, the end to end performance is improved by 5% to 30%
In this talk, I will illustrate how we implement these features and how they accelerate Apache Spark jobs.
Delivering Insights from 20M+ Smart Homes with 500M+ DevicesDatabricks
We started out processing big data using AWS S3, EMR clusters, and Athena to serve Analytics data extracts to Tableau BI.
However as our data and teams sizes increased, Avro schemas from source data evolved, and we attempted to serve analytics data through Web apps, we hit a number of limitations in the AWS EMR, Glue/Athena approach.
This is a story of how we scaled out our data processing and boosted team productivity to meet our current demand for insights from 20M+ Smart Homes and 500M+ devices across the globe, from numerous internal business teams and our 150+ CSP partners.
We will describe lessons learnt and best practices established as we enabled our teams with DataBricks autoscaling Job clusters and Notebooks and migrated our Avro/Parquet data to use MetaStore, SQL Endpoints and SQLA Console, while charting the path to the Delta lake…
A Collaborative Data Science Development WorkflowDatabricks
Collaborative data science workflows have several moving parts, and many organizations struggle with developing an efficient and scalable process. Our solution consists of data scientists individually building and testing Kedro pipelines and measuring performance using MLflow tracking. Once a strong solution is created, the candidate pipeline is trained on cloud-agnostic, GPU-enabled containers. If this pipeline is production worthy, the resulting model is served to a production application through MLflow.
10 Things Learned Releasing Databricks Enterprise WideDatabricks
Implementing tools, let alone an entire Unified Data Platform, like Databricks, can be quite the undertaking. Implementing a tool which you have not yet learned all the ins and outs of can be even more frustrating. Have you ever wished that you could take some of that uncertainty away? Four years ago, Western Governors University (WGU) took on the task of rewriting all of our ETL pipelines in Scala/Python, as well as migrating our Enterprise Data Warehouse into Delta, all on the Databricks platform. Starting with 4 users and rapidly growing to over 120 users across 8 business units, our Databricks environment turned into an entire unified platform, being used by individuals of all skill levels, data requirements, and internal security requirements.
Through this process, our team has had the chance and opportunity to learn while making a lot of mistakes. Taking a look back at those mistakes, there are a lot of things we wish we had known before opening the platform to our enterprise.
We would like to share with you 10 things we wish we had known before WGU started operating in our Databricks environment. Covering topics surrounding user management from both an AWS and Databricks perspective, understanding and managing costs, creating custom pipelines for efficient code management, learning about new Apache Spark snippets that helped save us a fortune, and more. We would like to provide our recommendations on how one can overcome these pitfalls to help new, current and prospective users to make their environments easier, safer, and more reliable to work in.
SQL Analytics Powering Telemetry Analysis at ComcastDatabricks
Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience.
In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses.
We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.
Empowering Zillow’s Developers with Self-Service ETLDatabricks
As the amount of data and the number of unique data sources within an organization grow, handling the volume of new pipeline requests becomes difficult. Not all new pipeline requests are created equal — some are for business-critical datasets, others are for routine data preparation, and others are for experimental transformations that allow data scientists to iterate quickly on their solutions.
To meet the growing demand for new data pipelines, Zillow created multiple self-service solutions that enable any team to build, maintain, and monitor their data pipelines. These tools abstract away the orchestration, deployment, and Apache Spark processing implementation from their respective users. In this talk, Zillow engineers discuss two internal platforms they created to address the specific needs of two distinct user groups: data analysts and data producers. Each platform addresses the use cases of its intended user, leverages internal services through its modular design, and empowers users to create their own ETL without having to worry about how the ETL is implemented.
Members of Zillow’s data engineering team discuss:
Why they created two separate user interfaces to meet the needs different user groups
What degree of abstraction from the orchestration, deployment, processing, and other ancillary tasks that chose for each user group
How they leveraged internal services and packages, including their Apache Spark package — Pipeler, to democratize the creation of high-quality, reliable pipelines within Zillow
How We Optimize Spark SQL Jobs With parallel and sync IODatabricks
Although NVMe has been more and more popular these years, a large amount of HDD are still widely used in super-large scale big data clusters. In a EB-level data platform, IO(including decompression and decode) cost contributes a large proportion of Spark jobs’ cost. In another word, IO operation is worth optimizing.
In ByteDancen, we do a series of IO optimization to improve performance, including parallel read and asynchronized shuffle. Firstly we implement file level parallel read to improve performance when there are a lot of small files. Secondly, we design row group level parallel read to accelerate queries for big-file scenario. Thirdly, implement asynchronized spill to improve job peformance. Besides, we design parquet column family, which will split a table into a few column families and different column family will be in different Parquets files. Different column family can be read in parallel, so the read performance is much higher than the existing approach. In our practice, the end to end performance is improved by 5% to 30%
In this talk, I will illustrate how we implement these features and how they accelerate Apache Spark jobs.
Delivering Insights from 20M+ Smart Homes with 500M+ DevicesDatabricks
We started out processing big data using AWS S3, EMR clusters, and Athena to serve Analytics data extracts to Tableau BI.
However as our data and teams sizes increased, Avro schemas from source data evolved, and we attempted to serve analytics data through Web apps, we hit a number of limitations in the AWS EMR, Glue/Athena approach.
This is a story of how we scaled out our data processing and boosted team productivity to meet our current demand for insights from 20M+ Smart Homes and 500M+ devices across the globe, from numerous internal business teams and our 150+ CSP partners.
We will describe lessons learnt and best practices established as we enabled our teams with DataBricks autoscaling Job clusters and Notebooks and migrated our Avro/Parquet data to use MetaStore, SQL Endpoints and SQLA Console, while charting the path to the Delta lake…
Accelerating Data Ingestion with Databricks AutoloaderDatabricks
Tracking which incoming files have been processed has always required thought and design when implementing an ETL framework. The Autoloader feature of Databricks looks to simplify this, taking away the pain of file watching and queue management. However, there can also be a lot of nuance and complexity in setting up Autoloader and managing the process of ingesting data using it. After implementing an automated data loading process in a major US CPMG, Simon has some lessons to share from the experience.
This session will run through the initial setup and configuration of Autoloader in a Microsoft Azure environment, looking at the components used and what is created behind the scenes. We’ll then look at some of the limitations of the feature, before walking through the process of overcoming these limitations. We will build out a practical example that tackles evolving schemas, applying transformations to your stream, extracting telemetry from the process and finally, how to merge the incoming data into a Delta table.
After this session you will be better equipped to use Autoloader in a data ingestion platform, simplifying your production workloads and accelerating the time to realise value in your data!
Large Scale Lakehouse Implementation Using Structured StreamingDatabricks
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...Databricks
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, Comcast, GrubHub, 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.
Automated Metadata Management in Data Lake – A CI/CD Driven ApproachDatabricks
We as data engineers are aware of trade off’s between development speed, metadata governance and schema evolution (or restriction) in rapidly evolving organization. Our day to day activities involve adding/removing/updating tables, protecting PII Information, curating and exposing data to our consumers. While our data lake keeps growing exponentially, there is equal increase in our downstream consumers. Struggle is to maintain balance between quickly promoting metadata changes with robust validation for downstream systems stability. In relational world DDL, DML changes can be managed through numerous options available for every kind of database from the vendor or 3rd party. As engineers we developed a tool which uses centralized git managed repository of data schemas in yml structure with ci/cd capabilities which maintains stability of our data lake and downstream systems.
In this presentation Northwestern Mutual Engineers, will discuss how they designed and developed new end-to-end ci/cd driven metadata management tool to make introduction of new tables/views, managing access requests etc in a more robust, maintainable and scalable way, all with only checking in yml files. This tool can be used by people who have no or minimal knowledge of spark.
Key focus will be:
Need for metadata management tool in a data lake
Architecture and Design of the tool
Maintaining information on databases/tables/views like schema, owner, PII, description etc in simple to understand yml structure
Live demo of creating a new table with CI/CD promotion to production
Is there a way that we can build our Azure Synapse Pipelines all with paramet...Erwin de Kreuk
Is there a way that we can build our Synapse Data Pipelines all with parameters all based on MetaData? Yes there's and I will show you how to. During this session I will show how you can load Incremental or Full datasets from your sql database to your Azure Data Lake. The next step is that we want to track our history from these extracted tables. We will do using Delta Lake. The last step that we want, is to make this data available in Azure SQL Database or Azure Synapse Analytics. Oh and we want to have some logging as well from our processes A lot to talk and to demo about during this session.
Leveraging Apache Spark and Delta Lake for Efficient Data Encryption at ScaleDatabricks
The increase in consumer data privacy laws brings continuing challenges to data teams all over the world which collect, store, and use data protected by these laws. The data engineering team at Mars Petcare is no exception, and in order to improve efficiency and accuracy in responding to these challenges they have built Gecko: an efficient, auditable, and simple CCPA compliance ecosystem designed for Spark and Delta Lake.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
ETL Made Easy with Azure Data Factory and Azure DatabricksDatabricks
Data Engineers are responsible for data cleansing, prepping, aggregating, and loading analytical data stores, which is often difficult and time-consuming. Azure Data Factory makes this work easy and expedites solution development. We’ll demonstrate how Azure Data Factory can enable a new UI-driven ETL design paradigm on top of Azure Databricks for building scaled-out data transformation pipelines.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks.
Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o
Presentation demo: https://github.com/devlace/azure-databricks-anomaly
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...Databricks
Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a “pure SaaS” company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems.
As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark.
In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism’s Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support interactive user experience.
Finally, we will share the future challenges for 2018 and beyond.
An introduction to using R in Power BI via the various touch points such as: R script data sources, R transformations, custom R visuals, and the community gallery of R visualizations
Data Distribution and Ordering for Efficient Data Source V2Databricks
More and more companies adopt Spark 3 to benefit from various enhancements and performance optimizations like adaptive query execution and dynamic partition pruning. During this process, organizations consider migrating their data sources to the newly added Catalog API (aka Data Source V2), which provides a better way to develop reliable and efficient connectors. Unfortunately, there are a few limitations that prevent unleashing the full potential of the Catalog API. One of them is the inability to control the distribution and ordering of incoming data that has a profound impact on the performance of data sources.
This talk is going to be useful for developers and data engineers that either develop their own or work with existing data sources in Spark. The presentation will start with an overview of the Catalog API introduced in Spark 3, followed by its benefits and current limitations compared to the old Data Source API. The main focus will be on an extension to the Catalog API developed in SPARK-23889, which lets implementations control how Spark distributes and orders incoming records before passing them to the sink.
The extension not only allows data sources to reduce the memory footprint during writes but also to co-locate data for faster queries and better compression. Apart from that, the introduced API paves the way for more advanced features like partitioned joins.
This one-hour presentation covers the tools and techniques for migrating SQL Server databases and data to Azure SQL DB or SQL Server on VM. Includes SSMA, DMA, DMS, and more.
Accelerating Data Ingestion with Databricks AutoloaderDatabricks
Tracking which incoming files have been processed has always required thought and design when implementing an ETL framework. The Autoloader feature of Databricks looks to simplify this, taking away the pain of file watching and queue management. However, there can also be a lot of nuance and complexity in setting up Autoloader and managing the process of ingesting data using it. After implementing an automated data loading process in a major US CPMG, Simon has some lessons to share from the experience.
This session will run through the initial setup and configuration of Autoloader in a Microsoft Azure environment, looking at the components used and what is created behind the scenes. We’ll then look at some of the limitations of the feature, before walking through the process of overcoming these limitations. We will build out a practical example that tackles evolving schemas, applying transformations to your stream, extracting telemetry from the process and finally, how to merge the incoming data into a Delta table.
After this session you will be better equipped to use Autoloader in a data ingestion platform, simplifying your production workloads and accelerating the time to realise value in your data!
Large Scale Lakehouse Implementation Using Structured StreamingDatabricks
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...Databricks
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, Comcast, GrubHub, 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.
Automated Metadata Management in Data Lake – A CI/CD Driven ApproachDatabricks
We as data engineers are aware of trade off’s between development speed, metadata governance and schema evolution (or restriction) in rapidly evolving organization. Our day to day activities involve adding/removing/updating tables, protecting PII Information, curating and exposing data to our consumers. While our data lake keeps growing exponentially, there is equal increase in our downstream consumers. Struggle is to maintain balance between quickly promoting metadata changes with robust validation for downstream systems stability. In relational world DDL, DML changes can be managed through numerous options available for every kind of database from the vendor or 3rd party. As engineers we developed a tool which uses centralized git managed repository of data schemas in yml structure with ci/cd capabilities which maintains stability of our data lake and downstream systems.
In this presentation Northwestern Mutual Engineers, will discuss how they designed and developed new end-to-end ci/cd driven metadata management tool to make introduction of new tables/views, managing access requests etc in a more robust, maintainable and scalable way, all with only checking in yml files. This tool can be used by people who have no or minimal knowledge of spark.
Key focus will be:
Need for metadata management tool in a data lake
Architecture and Design of the tool
Maintaining information on databases/tables/views like schema, owner, PII, description etc in simple to understand yml structure
Live demo of creating a new table with CI/CD promotion to production
Is there a way that we can build our Azure Synapse Pipelines all with paramet...Erwin de Kreuk
Is there a way that we can build our Synapse Data Pipelines all with parameters all based on MetaData? Yes there's and I will show you how to. During this session I will show how you can load Incremental or Full datasets from your sql database to your Azure Data Lake. The next step is that we want to track our history from these extracted tables. We will do using Delta Lake. The last step that we want, is to make this data available in Azure SQL Database or Azure Synapse Analytics. Oh and we want to have some logging as well from our processes A lot to talk and to demo about during this session.
Leveraging Apache Spark and Delta Lake for Efficient Data Encryption at ScaleDatabricks
The increase in consumer data privacy laws brings continuing challenges to data teams all over the world which collect, store, and use data protected by these laws. The data engineering team at Mars Petcare is no exception, and in order to improve efficiency and accuracy in responding to these challenges they have built Gecko: an efficient, auditable, and simple CCPA compliance ecosystem designed for Spark and Delta Lake.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
ETL Made Easy with Azure Data Factory and Azure DatabricksDatabricks
Data Engineers are responsible for data cleansing, prepping, aggregating, and loading analytical data stores, which is often difficult and time-consuming. Azure Data Factory makes this work easy and expedites solution development. We’ll demonstrate how Azure Data Factory can enable a new UI-driven ETL design paradigm on top of Azure Databricks for building scaled-out data transformation pipelines.
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks.
Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o
Presentation demo: https://github.com/devlace/azure-databricks-anomaly
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and...Databricks
Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a “pure SaaS” company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems.
As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark.
In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism’s Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support interactive user experience.
Finally, we will share the future challenges for 2018 and beyond.
An introduction to using R in Power BI via the various touch points such as: R script data sources, R transformations, custom R visuals, and the community gallery of R visualizations
Data Distribution and Ordering for Efficient Data Source V2Databricks
More and more companies adopt Spark 3 to benefit from various enhancements and performance optimizations like adaptive query execution and dynamic partition pruning. During this process, organizations consider migrating their data sources to the newly added Catalog API (aka Data Source V2), which provides a better way to develop reliable and efficient connectors. Unfortunately, there are a few limitations that prevent unleashing the full potential of the Catalog API. One of them is the inability to control the distribution and ordering of incoming data that has a profound impact on the performance of data sources.
This talk is going to be useful for developers and data engineers that either develop their own or work with existing data sources in Spark. The presentation will start with an overview of the Catalog API introduced in Spark 3, followed by its benefits and current limitations compared to the old Data Source API. The main focus will be on an extension to the Catalog API developed in SPARK-23889, which lets implementations control how Spark distributes and orders incoming records before passing them to the sink.
The extension not only allows data sources to reduce the memory footprint during writes but also to co-locate data for faster queries and better compression. Apart from that, the introduced API paves the way for more advanced features like partitioned joins.
This one-hour presentation covers the tools and techniques for migrating SQL Server databases and data to Azure SQL DB or SQL Server on VM. Includes SSMA, DMA, DMS, and more.
This presentation discusses SQL Server 2008 Migration tools, planning and execution. You will learn about the SQL Server Featuer Pack, the SQL Server Migration Assistant, and Performance Benchmarks of SQL Server 2005 vs. 2008.
DesignMind is located in Emeryville, California.
www.designmind.com
Introduction to QuerySurge Webinar
Wednesday, April 29th 2020 @11am ET
Eric Smyth, Director of Alliances
Bill Hayduk, CEO
Matt Moss, Product Manager
This is the slide deck for our webinar. Learn how QuerySurge automates the data validation and testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing.
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Objective
During this webinar, we demonstrate how QuerySurge solves the following challenges:
- Your need for data quality at speed
- How to automate your ETL testing process
- Your ability to test across your different data platforms
- How to integrate ETL testing into your DataOps pipeline
- How to analyze your data and pinpoint anomalies quickly
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Who should view this?
- ETL Developers /Testers
- Data Architects / Analysts
- DBAs
- BI Developers / Analysts
- IT Architects
- Managers of Data, BI & Analytics groups: CTOs, Directors, Vice Presidents, Project Leads
And anyone else with an interest in the Data & Analytics space who is interested in an automation solution for data validation & testing while improving data quality.
Sf big analytics_2018_04_18: Evolution of the GoPro's data platformChester Chen
Talk 1 : Evolution of the GoPro's data platform
In this talk, we will share GoPro’s experiences in building Data Analytics Cluster in Cloud. We will discuss: evolution of data platform from fixed-size Hadoop clusters to Cloud-based Spark Cluster with Centralized Hive Metastore +S3: Cost Benefits and DevOp Impact; Configurable, spark-based batch Ingestion/ETL framework;
Migration Streaming framework to Cloud + S3;
Analytics metrics delivery with Slack integration;
BedRock: Data Platform Management, Visualization & Self-Service Portal
Visualizing Machine learning Features via Google Facets + Spark
Speakers: Chester Chen
Chester Chen is the Head of Data Science & Engineering, GoPro. Previously, he was the Director of Engineering at Alpine Data Lab.
David Winters
David is an Architect in the Data Science and Engineering team at GoPro and the creator of their Spark-Kafka data ingestion pipeline. Previously He worked at Apple & Splice Machines.
Hao Zou
Hao is a Senior big data engineer at Data Science and Engineering team. Previously He worked as Alpine Data Labs and Pivotal
Introduction to SQL Server Analysis services 2008Tobias Koprowski
This is my presentation from 17th Polish SQL server User Group Meeting in Wroclaw. It\'s first part of Quadrology Bussiness Intelligence for ITPros Cycle.
Slides from the August 2021 St. Louis Big Data IDEA meeting from Sam Portillo. The presentation covers AWS EMR including comparisons to other similar projects and lessons learned. A recording is available in the comments for the meeting.
Apache Iceberg Presentation for the St. Louis Big Data IDEAAdam Doyle
Presentation on Apache Iceberg for the February 2021 St. Louis Big Data IDEA. Apache Iceberg is an alternative database platform that works with Hive and Spark.
Slides from the January 2021 St. Louis Big Data IDEA meeting by Tim Bytnar regarding using Docker containers for a localized Hadoop development cluster.
Slides from the December 2019 St. Louis Big Data IDEA meetup group. Jon Leek discussed how the St. Louis Regional Data Alliance ingests, stores, and reports on their data.
Tailoring machine learning practices to support prescriptive analyticsAdam Doyle
Slides from the November St. Louis Big Data IDEA. Anthony Melson talked about how to engineer machine learning practices to better support prescriptive analytics.
Data Engineering and the Data Science LifecycleAdam Doyle
Everyone wants to be a data scientist. Data modeling is the hottest thing since Tickle Me Elmo. But data scientists don’t work alone. They rely on data engineers to help with data acquisition and data shaping before their model can be developed. They rely on data engineers to deploy their model into production. Once the model is in production, the data engineer’s job isn’t done. The model must be monitored to make sure that it retains its predictive power. And when the model slips, the data engineer and the data scientist need to work together to correct it through retraining or remodeling.
Data engineering Stl Big Data IDEA user groupAdam Doyle
Modern day Data Engineering requires creating reliable data pipelines, architecting distributed systems, designing data stores, and preparing data for other teams.
We’ll describe a year in the life of a Data Engineer who is tasked with creating a streaming data pipeline and touch on the skills necessary to set one up using Apache Spark.
Slides from the April 2019 meeting of the St. Louis Big Data IDEA meetup.
Big Data Retrospective - STL Big Data IDEA Jan 2019Adam Doyle
Slides from the STL Big Data IDEA meeting from January 2019. The presenters discussed technologies to continue using, stop using, and start using in 2019.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
4. Data Store Engineer
• Store Data, Retrieve Data, Optimize Data
• SQL (all flavors)
• Data Warehouse
• Data Lake
5. ETL Engineer
• Retrieve data from remote sources and move the data into a data
store.
• Data Enrichment
• Tool-based ETL products
• Programmatic ETL development
6. Stream Engineer
• Retrieve data from streaming data sources
• Handle multi-source and late-arriving data
• Stream data sources (Kafka, RabbitMQ)
• Programmatic processing (Spark)
7. Data Quality Engineer
• Profile and check for outliers
• Handle data quality issues
• Data Quality Tools (Informatica DQ, Great Expectations)
• Data Analysis/Profiling (SQL)
• Programmatic Adjustments
8. Visualization Engineer
• Develop internal data models within data visualization tools
• Create dashboards
• Data Visualization tools (Tableau, Power BI)
• Data analysis (SQL)
9. Deployment Engineer
• Deploys processes to production
• DevOps, CI/CD (Ansible/Terraform)
• Source Control (Git)
• Data deployment (Liquibase)
10. Operations Engineer
• Monitor data applications
• Troubleshooting production issues
• Data Analysis (SQL)
• Root Cause Analysis (Splunk)
11. Production Engineer
• Ensure that application code is ready to go to production
• Test Harness (SoapUI)
• Programming languages
• Understanding of Machine Learning processes
12. Cluster Engineer
• Work with clustered hardware and software to ensure deployment
and scalability.
• Cluster software (Hadoop, Kubernetes)
• Log Monitoring (Splunk)
13. Cloud Engineer
• Implement solutions in the cloud with both cloud-native technology
and conversions of on-premise solutions
• Cloud Platforms (Azure, AWS, GCP)
• Infrastructure as Code (Terraform)
14. Machine Learning Engineer
• Adapt Machine Learning Models to be deployed in production with
an emphasis on performance and scalability
• Machine Learning Platforms (Spark)
• Programming language (Python, Scala)
• Performance tuning
15. Feature Engineer
• Create informational features to be used in data science models – at
scale, at speed
• Extract information form data
• Aggredate data into information
• Apply business rules
• Data analysis (SQL)
• Programming language (Python, Scala)
