The document summarizes John Ward's presentation on report development using BIRT (Business Intelligence and Reporting Tools) at the 6th Annual EclipseCon. It includes an agenda that covers an introduction to BIRT, building reports, working with data and charts, report formatting, and event handling. It also discusses reuse of components through templates and libraries, deployment of reports, and extensions to BIRT's functionality.
Serverless Kafka and Spark in a Multi-Cloud Lakehouse ArchitectureKai Wähner
Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable.
Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden.
This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents.
We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse.
Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
Radical Speed for SQL Queries on Databricks: Photon Under the HoodDatabricks
Join this session to hear from the Photon product and engineering team talk about the latest developments with the project.
As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to.
Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution.
Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Storing time series data with Apache CassandraPatrick McFadin
If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
This is my presentation at SQLBits 8, Brighton, 9th April 2011. This session is about advanced dimensional modelling topics such as Fact Table Primary Key, Vertical Fact Tables, Aggregate Fact Tables, SCD Type 6, Snapshotting Transaction Fact Tables, 1 or 2 Dimensions, Dealing with Currency Rates, When to Snowflake, Dimensions with Multi Valued Attributes, Transaction-Level Dimensions, Very Large Dimensions, A Dimension With Only 1 Attribute, Rapidly Changing Dimensions, Banding Dimension Rows, Stamping Dimension Rows and Real Time Fact Table. Prerequisites: You need have a basic knowledge of dimensional modelling and relational database design.
My name is Vincent Rainardi. I am a data warehouse & BI architect. I wrote a book on SQL Server data warehousing & BI, as well as many articles on my blog, www.datawarehouse.org.uk. I welcome questions and discussions on data warehousing on vrainardi@gmail.com. Enjoy the presentation.
Serverless Kafka and Spark in a Multi-Cloud Lakehouse ArchitectureKai Wähner
Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable.
Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden.
This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents.
We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse.
Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
Radical Speed for SQL Queries on Databricks: Photon Under the HoodDatabricks
Join this session to hear from the Photon product and engineering team talk about the latest developments with the project.
As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to.
Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution.
Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Storing time series data with Apache CassandraPatrick McFadin
If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
This is my presentation at SQLBits 8, Brighton, 9th April 2011. This session is about advanced dimensional modelling topics such as Fact Table Primary Key, Vertical Fact Tables, Aggregate Fact Tables, SCD Type 6, Snapshotting Transaction Fact Tables, 1 or 2 Dimensions, Dealing with Currency Rates, When to Snowflake, Dimensions with Multi Valued Attributes, Transaction-Level Dimensions, Very Large Dimensions, A Dimension With Only 1 Attribute, Rapidly Changing Dimensions, Banding Dimension Rows, Stamping Dimension Rows and Real Time Fact Table. Prerequisites: You need have a basic knowledge of dimensional modelling and relational database design.
My name is Vincent Rainardi. I am a data warehouse & BI architect. I wrote a book on SQL Server data warehousing & BI, as well as many articles on my blog, www.datawarehouse.org.uk. I welcome questions and discussions on data warehousing on vrainardi@gmail.com. Enjoy the presentation.
Solving Data Discovery Challenges at Lyft with Amundsen, an Open-source Metad...Databricks
Amundsen is the data discovery metadata platform that originated from Lyft which is recently donated to Linux Foundation AI. Since its open-sourced, Amundsen has been used and extended by many different companies within our community.
Better than you think: Handling JSON data in ClickHouseAltinity Ltd
Robert Hodges shows how ClickHouse, a relational database with tables, can offer high-performance analysis of JSON data. This talk provides a cookbook of schema design, indexing, data loading, and query tricks we gave learned over years of helping users build analytical apps for servicds logs, observability data, financial transactions, and other types of semi-structured data. Robert Hodges is CEO of Altinity and a certified database geek.
https://altinity.com
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup
Denodo Data Virtualization Platform architecture: Data Discovery and Data Gov...Denodo
Being able to discover and manage your data are essential features of a modern Data Virtualization platform. The Denodo Platform offers a wealth a capabilities for discovering and managing data and metadata and provides advanced data governance functionality, such as data lineage and change impact analysis. This webinar will examine these capabilities.
More information and FREE registrations to this webinar: http://goo.gl/8U4ynC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
Introspection and Metadata Management
Global Search
Data Governance
All About JSON and ClickHouse - Tips, Tricks and New Features-2022-07-26-FINA...Altinity Ltd
JSON is the king of data formats and ClickHouse has a plethora of features to handle it. This webinar covers JSON features from A to Z starting with traditional ways to load and represent JSON data in ClickHouse. Next, we’ll jump into the JSON data type: how it works, how to query data from it, and what works and doesn’t work. JSON data type is one of the most awaited features in the 2022 ClickHouse roadmap, so you won’t want to miss out. Finally, we’ll talk about Jedi master techniques like adding bloom filter indexing on JSON data.
Building Robust ETL Pipelines with Apache SparkDatabricks
Stable and robust ETL pipelines are a critical component of the data infrastructure of modern enterprises. ETL pipelines ingest data from a variety of sources and must handle incorrect, incomplete or inconsistent records and produce curated, consistent data for consumption by downstream applications. In this talk, we’ll take a deep dive into the technical details of how Apache Spark “reads” data and discuss how Spark 2.2’s flexible APIs; support for a wide variety of datasources; state of art Tungsten execution engine; and the ability to provide diagnostic feedback to users, making it a robust framework for building end-to-end ETL pipelines.
A trigger is an Apex script that executes before or after certain events occur in DML, For example, before the object record is entered into the database or after the record has been deleted. Triggers allow you to carry out custom actions before or after changes to Salesforce records.
With Apache Kafka 0.9, the community has introduced a number of features to make data streams secure. In this talk, we’ll explain the motivation for making these changes, discuss the design of Kafka security, and explain how to secure a Kafka cluster. We will cover common pitfalls in securing Kafka, and talk about ongoing security work.
Real-time Analytics with Trino and Apache PinotXiang Fu
Trino summit 2021:
Overview of Trino Pinot Connector, which bridges the flexibility of Trino's full SQL support to the power of Apache Pinot's realtime analytics, giving you the best of both worlds.
Common Strategies for Improving Performance on Your Delta LakehouseDatabricks
The Delta Architecture pattern has made the lives of data engineers much simpler, but what about improving query performance for data analysts? What are some common places to look at for tuning query performance? In this session we will cover some common techniques to apply to our delta tables to make them perform better for data analysts queries. We will look at a few examples of how you can analyze a query, and determine what to focus on to deliver better performance results.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
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.
Spark (Structured) Streaming vs. Kafka StreamsGuido Schmutz
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. In this session we compare two popular Streaming Analytics solutions: Spark Streaming and Kafka Streams.
Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala.
Kafka Streams is the stream processing solution which is part of Kafka. It is provided as a Java library and by that can be easily integrated with any Java application.
This presentation shows how you can implement stream processing solutions with each of the two frameworks, discusses how they compare and highlights the differences and similarities.
Solving Data Discovery Challenges at Lyft with Amundsen, an Open-source Metad...Databricks
Amundsen is the data discovery metadata platform that originated from Lyft which is recently donated to Linux Foundation AI. Since its open-sourced, Amundsen has been used and extended by many different companies within our community.
Better than you think: Handling JSON data in ClickHouseAltinity Ltd
Robert Hodges shows how ClickHouse, a relational database with tables, can offer high-performance analysis of JSON data. This talk provides a cookbook of schema design, indexing, data loading, and query tricks we gave learned over years of helping users build analytical apps for servicds logs, observability data, financial transactions, and other types of semi-structured data. Robert Hodges is CEO of Altinity and a certified database geek.
https://altinity.com
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup
Denodo Data Virtualization Platform architecture: Data Discovery and Data Gov...Denodo
Being able to discover and manage your data are essential features of a modern Data Virtualization platform. The Denodo Platform offers a wealth a capabilities for discovering and managing data and metadata and provides advanced data governance functionality, such as data lineage and change impact analysis. This webinar will examine these capabilities.
More information and FREE registrations to this webinar: http://goo.gl/8U4ynC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
Introspection and Metadata Management
Global Search
Data Governance
All About JSON and ClickHouse - Tips, Tricks and New Features-2022-07-26-FINA...Altinity Ltd
JSON is the king of data formats and ClickHouse has a plethora of features to handle it. This webinar covers JSON features from A to Z starting with traditional ways to load and represent JSON data in ClickHouse. Next, we’ll jump into the JSON data type: how it works, how to query data from it, and what works and doesn’t work. JSON data type is one of the most awaited features in the 2022 ClickHouse roadmap, so you won’t want to miss out. Finally, we’ll talk about Jedi master techniques like adding bloom filter indexing on JSON data.
Building Robust ETL Pipelines with Apache SparkDatabricks
Stable and robust ETL pipelines are a critical component of the data infrastructure of modern enterprises. ETL pipelines ingest data from a variety of sources and must handle incorrect, incomplete or inconsistent records and produce curated, consistent data for consumption by downstream applications. In this talk, we’ll take a deep dive into the technical details of how Apache Spark “reads” data and discuss how Spark 2.2’s flexible APIs; support for a wide variety of datasources; state of art Tungsten execution engine; and the ability to provide diagnostic feedback to users, making it a robust framework for building end-to-end ETL pipelines.
A trigger is an Apex script that executes before or after certain events occur in DML, For example, before the object record is entered into the database or after the record has been deleted. Triggers allow you to carry out custom actions before or after changes to Salesforce records.
With Apache Kafka 0.9, the community has introduced a number of features to make data streams secure. In this talk, we’ll explain the motivation for making these changes, discuss the design of Kafka security, and explain how to secure a Kafka cluster. We will cover common pitfalls in securing Kafka, and talk about ongoing security work.
Real-time Analytics with Trino and Apache PinotXiang Fu
Trino summit 2021:
Overview of Trino Pinot Connector, which bridges the flexibility of Trino's full SQL support to the power of Apache Pinot's realtime analytics, giving you the best of both worlds.
Common Strategies for Improving Performance on Your Delta LakehouseDatabricks
The Delta Architecture pattern has made the lives of data engineers much simpler, but what about improving query performance for data analysts? What are some common places to look at for tuning query performance? In this session we will cover some common techniques to apply to our delta tables to make them perform better for data analysts queries. We will look at a few examples of how you can analyze a query, and determine what to focus on to deliver better performance results.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
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.
Spark (Structured) Streaming vs. Kafka StreamsGuido Schmutz
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. In this session we compare two popular Streaming Analytics solutions: Spark Streaming and Kafka Streams.
Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala.
Kafka Streams is the stream processing solution which is part of Kafka. It is provided as a Java library and by that can be easily integrated with any Java application.
This presentation shows how you can implement stream processing solutions with each of the two frameworks, discusses how they compare and highlights the differences and similarities.
IC™ 2018 Powerful tools for your multi-disciplinary team
Featuring new Ternary Plots and Drafting toolkits, IC™ 2018 provides greater confidence to any interpretation using well-data. The improved User Controls, new direct Feedback form and additional IP integration builds on the strong communication toolkits available for any multi-disciplinary team.
The Tableau Course at the ETLhive ensures an in-depth understanding of the Tableau Desktop Skills, whereby you will gain a meaningful insight into Data Visualization, conditional formatting, scripting, and organising and simplifying data. Further, the course throws light on Tableau Statistics,
Everything You Need to Know About Oracle 12c IndexesSolarWinds
Indexes are important to consider for optimal performance in every Oracle database. However with each new release, there is an incredible amount of new features and/or changes which can impact how Oracle indexes function and are maintained. This often results in many applications running inefficiently. In this presentation, we will review current Oracle index structures/options and discuss how they work, when they should be used and how they should be maintained.
NBITS provides the SAP BODS training in Hyderabad. It can provides online and classroom training by real time industrial experts. It can provide also the job assistance. BODS stand for Business objects Data services. Bods is one of the part of SAP.
This presentation introduces Kicktag and the Cosmos reporting platform - this is the perfect place to start if you haven't worked with us before, and there are a number of references for further reading.
In this deck, we present an outline of the Cosmos platform including how the reporting modules and data integration tools work together. There are a number of visual examples ranging from basic document libraries to real-time analytics dashboards and bespoke mobile business discovery portals.
Mycadtools is a software suite consisting of more than 50 utilities fully integrated with Solidworks. It adapts the CAD tool to your organization and work methods, reduces time wasted due to CAD data management in favour of design and innovation and enriches the basic CAD capabilities by taking into account your specific business requirements
Jeremy Tammik, Forge Program Development
Combine the Forge Data Management, Model Derivative and Viewer APIs with REST and socket.io to implement a real-time round-trip BIM editor. Selected data only! Free choice!
Apache CarbonData+Spark to realize data convergence and Unified high performa...Tech Triveni
Challenges in Data Analytics:
Different application scenarios need different storage solutions: HBASE is ideal for point query scenarios but unsuitable for multi-dimensional queries. MPP is suitable for data warehouse scenarios but engine and data are coupled together which hampers scalability. OLAP stores used in BI applications perform best for Aggregate queries but full scan queries perform at a sub-optimal performance. Moreover, they are not suitable for real-time analysis. These distinct systems lead to low resource sharing and need different pipelines for data and application management.
Two of the tech industry’s essential front runners providing business intelligence solutions are Microsoft’s Power BI and Tableau. These leaders of data visualization help businesses narrow down and analyze their data with powerful built-in tools and clear visualizations. Each platform has distinctive strengths and weaknesses that should be considered before deciding on a business intelligence software.
Affirma is a top Business Intelligence Consultant:
http://www.affirmaconsulting.com/our-services/business-intelligence-consulting/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
2. BIRT and Us – Who am I? Who are you?
• Who am I?
• John Ward, BIRT user
• Independent BIRT Enthusiast
• Author: Practical Data Analysis and Reporting with BIRT
• http://digiassn.blogspot.com
• john@wardmail.com
• Who are you?
• Name, Company, technical background (C, SQL, Java, reporting…)
• Experience with BIRT
• What are you hoping to get out of today’s presentation?
2
2
5. Agenda
Outline of class
• BIRT
• History
• Guided Tour
• The Report Perspective
• Building Reports
• Components
• Exercise
• Data
• Exercise
• Charts and Layouts
• Exercise
• Q&A
6. What is BIRT
• Brief history and background
• Whos Behind It
• Who uses it
7. The BIRT Community at a
Glance
• Project Committers
• Actuate (http://www.actuate.com)
• IBM (http://www.ibm.com)
• Inetsoft (http://www.inetsoft.com)
• BIRT pages on Eclipse web site
• http://www.eclipse.org/birt
• Download BIRT and walk through a tutorial
• BIRT Help
• http://www.birt-exchange.com
9
8. High Level BIRT Architecture
10
Report Designer Report Engine
Presentation
ServicesReport Design Engine
XML
Report
Design
Report
Document
HTML
PDF
Print
Eclipse
Report
Designer
Generation
Services
Data
Transform.
Services
Charting
Engine
Data
Data
Chart
Designer
Eclipse
DTP
ODA
Custom
Designer
11. Report Designer
• Area where report layout is designed, previews of the report are seen, scripts and events are
coded, and XML source for report can be viewed.
12. Palette
• Contains a number of visual
elements used in BIRT report designs
• Similar to Toolboxes in MS Visual
Studio and other visual IDE
13. Data Explorer
• Contains.
•Data Sources: Connections to
database.
•Data Sets: Same as a Record set.
Allows users to navigate through
Query Results.
•Report Parameters: Used as the
interface for report parameters that
will be passed to a Data Set.
14. Navigator
• Navigates through Eclipse projects and
files under those projects
• Allows user to run report inside of BIRT
15. Outline
• Provides hierarchy of current report
• Allows for easy navigation of report
elements to change properties in the
property pane
• When selecting libraries, use the outline
to copy non-visual components such as
data sources and sets
16. Property Editor
• Properties Pane.
• Properties for BIRT objects can be modified, such as Data Set Binding, Font information,
formatting for Data Set returns such as Row Highlighting and Color Alternation.
20. Tables/Lists
• Slots
• Header
• Detail
• Footer
• Data Binding
• Makes table/list aware of values
in Data Set
• Grouping
• Group on value in Data Set
• Sorting
• Sort on value in Data Set
• Rows
• Columns
• Cells
22
21. Report Items
• Label
• Dumb, static
• Text
• HTML, RTF
• Data
• Smart, can be Dynamic, Expression Driven
• Chart
• Graphics for the small words and pictures crowd
(management)
• Aggregation
• Total, Sum, Avg
22. Expressions
• A lot of things in BIRT are based on them
• Report Items
• Properties
• Outputs
• Formatting
• Small snippets of Javascript that return single values
• You will see these everywhere
• Anytime you start off with row[“ColumnName”], you are starting
with an expression.
• Anywhere there is an expression editor.
23. Exercise 1 – Introduction to BIRT
Environment (30 minutes)
• What you will learn
• Get familiar with the
environment.
• Learn basic report layout
• Build basic report
24. Introduction to Data Elements
• What are the data elements?
• Where do you access them?
• How do they work?
25. Data Access
• Data Sources
• Handled data connections
• Data Sets
• Defined Data to be used in Report
• Parameters
• Used for User Interaction to Filter
Data
• Cascading Parameters
• Parameter Binding
27
26. Data Source Types
• Out of the Box
• JDBC
• Web Service
• Flat File
• Scripted Data Source
• Extensions
• Anything BIRT doesn’t do out of the box
• Data Cube
• Analytics based on data sets
• Lets you cut and slice data
27. 29
Exercise 2 – Working with
Data (30 minutes)
Work with Data In a report
Work with Flat File and Database driven
reports
Formatting Reports on a basic level
28. 30
Charting in Reports
• Charts integrated with BIRT Report Designer
• Charts can be used standalone as a library
• Wide variety of chart types
• Many formatting options
• Charts are extensible to include more types
40. Formatting
• Intrinsic Properties
• At the level of the element itself
• Overrides CSS
• CSS
• Used for applying a global style to reports
• Cascades down.
• Lowest element wins
• Similar to HTML CSS, but not exactly the same
41. Using styles
• Defined in
• Report design
• Library
• Separate CSS text file
• Can use pre-defined element names or custom names
• Ex.
• .table { background : #FFFFFF }
• Will apply to all tables in report
• Use to keep look and feel of multiple reports consistent
42. Using Intrinsic Properties
• Very tempting to use since it is the first way you learn to set
styles
• Only do it in small reporting projects
• Tend to be more cumbersome to maintain
43. Order of Application
Tab le (Font-Size = 8pt, defined in style)
Row
Cell
Data (Style Defined Font Size: 7pt)
Data (Style Defined Font Size: 7pt, Intrinsic 10pt)
44. Exercise 3 – Charting and
Advanced Formatting
• Build some real
reports
• Work with charts
• Work with styles and
layouts
45. Event handling
• Working with the Report Lifecycle
• Alters the flow of events
• Used to do things like dynamically add elements to a report,
scripts data sources, and work with the chart engine
• Very advanced topic!!
46. Reuse and Teams
• How do you re-use components?
• What do you do when you work in a team so each member
isn’t duplicating the same data sources, data sets, and report
headers?
• How do you set up common starting points?
47. Templates
• A common starting point for report development.
• Is basically a report design that is registered with the New
Report Wizard.
• Can contains as little or as much as needed in order to start a
report
• Data sources
• Data sets
• Report items and containers
• Master page elements
48. Libraries
• Contains shared components
• When a library item is updated, all dependent reports are
updated as well.
• Requires deployment with reports.
49. Deployment
• Complex topic
• Can deploy to J2EE containers, applications, or the BIRT Web
Viewer
• Commercial Actuate has tools built in for handling this.
• Can integrate BIRT engine into your application. See
“Integrating and Extending BIRT” for more information
• Later sessions at EclipseCon will discuss this more in depth as
well.