Sap Hana is an in-memory database platform developed by SAP that combines database, application processing, and integration services. It allows for both high transaction rates and complex query processing on the same platform. Sap Hana Online training provides certification training covering topics such as the Hana architecture, data modeling, security, reporting, and data provisioning to help aspirants learn and work with the Sap Hana platform. The training includes materials like videos, documentation, and access to the Hana platform.
SAP Business Suite (S/4) HANA Online Training is offering at Glory IT Technologies by Real time Certified Expert. SAP S/4 HANA the next-generation business suite to help customers run simple. SAP S/4HANA is a new product fully built on the advanced in-memory platform, SAP HANA and is designed on the most modern design principles with the SAP Fiori user experience (UX) for mobile devices.
This document provides an overview of SAP HANA 1.0 SP10 and its key components. It discusses the architecture of SAP HANA, including its in-memory column-oriented database engine. It also covers various topics like modeling, reporting, administration, data provisioning, user management and security in SAP HANA. Specific applications that can leverage SAP HANA are also summarized, such as SAP BW, COPA, and SAP S/4HANA.
The document discusses Actuate's SAP R/3 Connector. It provides access to SAP R/3 data without needing ABAP expertise. Developers can leverage SQL skills and build reports using a graphical query editor. The connector was built on Actuate's Open Data Access framework using Java and is integrated with Actuate's reporting tools. It eliminates the need to program in ABAP and allows direct table access using Open-SQL.
The document discusses an SAP HANA 1.0 SPS 10 training provided by Visu Inotech. It covers topics such as the architecture and modeling capabilities of SAP HANA, reporting with SAP HANA, data provisioning and loading, administration of SAP HANA including user management and security, using SAP HANA with other SAP products like COPA and BW, and an introduction to SAP S/4HANA. The training aims to provide an overview of these areas and capabilities of SAP HANA.
The document discusses how to add a system in the SAP HANA Studio Administration Console. It explains that the Administration Console allows users to perform administrative tasks like monitoring, user management, and more. It then outlines the steps to add a system: right-click on the left panel and select "Add System", enter the database hostname, instance number, and description, and click "Next" and "Finish" after entering valid credentials.
This document provides an overview of new and exciting features in Power BI. It discusses the new data gateway, large model capabilities, hybrid tables in Power BI Premium, visualizing data from Power Apps and Dynamics 365 apps, the Data Analyst Associate certification, goals functionality, Teams integration, deployment options for developers, the new Power BI Windows app, and instructor-led training courses.
Sap Hana is an in-memory database platform developed by SAP that combines database, application processing, and integration services. It allows for both high transaction rates and complex query processing on the same platform. Sap Hana Online training provides certification training covering topics such as the Hana architecture, data modeling, security, reporting, and data provisioning to help aspirants learn and work with the Sap Hana platform. The training includes materials like videos, documentation, and access to the Hana platform.
SAP Business Suite (S/4) HANA Online Training is offering at Glory IT Technologies by Real time Certified Expert. SAP S/4 HANA the next-generation business suite to help customers run simple. SAP S/4HANA is a new product fully built on the advanced in-memory platform, SAP HANA and is designed on the most modern design principles with the SAP Fiori user experience (UX) for mobile devices.
This document provides an overview of SAP HANA 1.0 SP10 and its key components. It discusses the architecture of SAP HANA, including its in-memory column-oriented database engine. It also covers various topics like modeling, reporting, administration, data provisioning, user management and security in SAP HANA. Specific applications that can leverage SAP HANA are also summarized, such as SAP BW, COPA, and SAP S/4HANA.
The document discusses Actuate's SAP R/3 Connector. It provides access to SAP R/3 data without needing ABAP expertise. Developers can leverage SQL skills and build reports using a graphical query editor. The connector was built on Actuate's Open Data Access framework using Java and is integrated with Actuate's reporting tools. It eliminates the need to program in ABAP and allows direct table access using Open-SQL.
The document discusses an SAP HANA 1.0 SPS 10 training provided by Visu Inotech. It covers topics such as the architecture and modeling capabilities of SAP HANA, reporting with SAP HANA, data provisioning and loading, administration of SAP HANA including user management and security, using SAP HANA with other SAP products like COPA and BW, and an introduction to SAP S/4HANA. The training aims to provide an overview of these areas and capabilities of SAP HANA.
The document discusses how to add a system in the SAP HANA Studio Administration Console. It explains that the Administration Console allows users to perform administrative tasks like monitoring, user management, and more. It then outlines the steps to add a system: right-click on the left panel and select "Add System", enter the database hostname, instance number, and description, and click "Next" and "Finish" after entering valid credentials.
This document provides an overview of new and exciting features in Power BI. It discusses the new data gateway, large model capabilities, hybrid tables in Power BI Premium, visualizing data from Power Apps and Dynamics 365 apps, the Data Analyst Associate certification, goals functionality, Teams integration, deployment options for developers, the new Power BI Windows app, and instructor-led training courses.
The document outlines the topics covered in a training course on SAP HANA 1.0 SPS 10. It includes an overview of the architecture of SAP HANA including its row and column storage, modeling in SAP HANA including creating different types of views and best practices. Administration topics like backup/recovery, user management and security are also summarized. The course further describes reporting tools that can connect to SAP HANA and loading of data from different sources into HANA. It also discusses SAP solutions that use HANA like COPA, BW and S4HANA.
InfoPath has limitations for advanced forms that require repeating sections, reporting on data, and integrating with external databases. Moving data to a SQL database addresses these issues and enables enterprise-level functionality like centralized data storage, improved reporting, and faster queries. A data-driven web service approach allows InfoPath forms to securely submit and query data from any SQL database without requiring custom code or changes to the database schema. All form templates can leverage a single shared service.
Integrating Customized Reports, Dashboards & Analysis into Your ApplicationMia Yuan Cao
Learn how to enhance your applications and products with seamlessly embedded reporting, dashboards, and analysis capabilities. Customize reports and dashboards to blend into your application's UI, as well as integrate JReport's reporting engine into your backend application server, security, and admin systems. Join us to see how your business users can create sophisticated ad hoc dashboards and reports, all on their own.
Webinar & Demo Highlights:
- Easy creation of reports and dashboards with advanced data visualization
- Embedding strategies for a wide-range of deployments
- Multi-dimensional view of your data with Visual Analysis
Embedded BI Advanced Data Visualization and Analysis into Any ApplicationJReport
This document discusses Jinfonet's JReport, an embeddable data visualization and analysis platform. JReport allows embedding advanced reporting, dashboards, and ad hoc analysis capabilities into applications. It offers high performance in-memory cubes, server clustering, and low learning curves. JReport can be embedded into on-premises or cloud-based applications and has various licensing models. It supports connecting to different data sources and empowering various user roles from application developers to business users.
This document outlines the course content for Embedded BPC, which includes an overview of Embedded BPC, basics of SAP BW4HANA, modeling objects in BWMT, loading and querying master and transaction data, planning with BPC Embedded, configuring advanced data store objects and planning functions, creating queries, analysis in Excel and Analysis for Office, advanced planning features like characteristic relations and data slices, designing planning sequences and top-down distribution, building FOX formulas, using the BPC web client, modeling environments and maintaining BW master data, process control, and transporting objects.
This document provides a summary of an individual's work experience from 2004-2006 as a project manager and system designer at GENESIIS Software. It lists 18 projects they worked on in areas including GIS, drought monitoring, irrigation mapping, and various web applications. Their responsibilities included project management, system design, development, implementation, and client coordination. Skills included languages like C/C++, Java, ASP.NET, and tools like SQL Server, PHP, and ESRI mapping products.
Nishant Kumar is an ABAP consultant with over 10 years of experience in SAP modules including FI, CO, MM, and SD. He has extensive skills in ABAP/4 programming, reporting, interfaces, and custom development. Some of his projects include developing interactive ALV reports, classical reports, BDC programs, BADI implementations, and more. He is proficient in OOABAP, performance tuning, and has worked on an SAP ECC 6.0 project for residential quarter management.
The document discusses SAP HANA, an in-memory database from SAP. It provides an overview of SAP HANA, including its introduction, hardware innovations to address bottlenecks, data storage approaches, scenarios for using SAP HANA, and licensing and implementation landscape considerations. The document also mentions data provisioning methods for SAP HANA like data replication, uploading files, and using SAP BODS (Business Objects Data Services).
Nishant Kumar has over 10 years of experience working as an ABAP consultant. He has extensive skills in ABAP/4 programming, reporting, interfaces, and dialog programming. Some projects he has worked on include developing interactive ALV reports, module pool programs, and BDC programs for uploading master data. He has also created custom BADIs and BAPIs. Nishant holds a Bachelor's degree in computer science and has a strong foundation in OOABAP, databases, and SAP modules.
This document outlines the content of a course on BIG DATA & HADOOP. The course is intended for developers and data warehouse professionals and will provide both lectures and hands-on experience working with Hadoop. It will cover key concepts like HDFS, MapReduce, YARN, and popular Hadoop ecosystems including Hive, Pig, HBase, Spark, and Impala. Lectures will be complemented by assignments and exercises working directly in a local virtual Hadoop cluster to gain experience administering, configuring, and troubleshooting Hadoop systems.
The document discusses OLAP (Online Analytical Processing) and summarizes key concepts. It describes different types of OLAP including MOLAP, ROLAP, and HOLAP and compares their functions and advantages/disadvantages. It also discusses OLAP operations like selection, roll-up, drill-down, and drill-across. The document concludes by discussing applications of OLAP and future directions such as integrating OLAP with data mining and improving security and user interaction.
OLAP provides multidimensional analysis of large datasets to help solve business problems. It uses a multidimensional data model to allow for drilling down and across different dimensions like students, exams, departments, and colleges. OLAP tools are classified as MOLAP, ROLAP, or HOLAP based on how they store and access multidimensional data. MOLAP uses a multidimensional database for fast performance while ROLAP accesses relational databases through metadata. HOLAP provides some analysis directly on relational data or through intermediate MOLAP storage. Web-enabled OLAP allows interactive querying over the internet.
Pr dc 2015 sql server is cheaper than open sourceTerry Bunio
SQL Server was found to be cheaper than open source options for a data warehouse project with the following requirements:
- Serve 100% operational reports from 1TB of data
- No need for advanced features like big data support
- Requirement was for basic textual reporting
An investigation was conducted of SQL Server, Oracle, Sybase, MySQL, and PostgreSQL. SQL Server and PostgreSQL were evaluated further based on costs and functionality. After a 10 year total cost of ownership analysis, SQL Server was found to be cheaper despite having a higher initial license cost. The lessons learned were that open source options are not always cheaper, to test options yourself rather than rely on biased reports, and that Oracle is very expensive.
This document provides tips for optimizing performance in Power BI by focusing on different areas like data sources, the data model, visuals, dashboards, and using trace and log files. Some key recommendations include filtering data early, keeping the data model and queries simple, limiting visual complexity, monitoring resource usage, and leveraging log files to identify specific waits and bottlenecks. An overall approach of focusing on time-based optimization by identifying and addressing the areas contributing most to latency is advocated.
This presentation explains the three layer API design which organisations can use to get most out of there systems with less development and maintenance time spent on fixing issues as a whole in org.
The document discusses the architecture of Oracle Business Intelligence (OBIEE). It describes the key components including the BI Server, BI Scheduler, Repository, and data sources. It explains how queries from clients are processed, cached, and passed to underlying databases. It also covers security approaches like data level security and object level security. Repository (.rpd) files are described as containing all metadata and security rules to define OBIEE solutions. Approaches to OLAP like ROLAP, MOLAP, and hybrid OLAP are also summarized.
Blackboard DevCon 2011 - Developing B2 for Performance and ScalabilityNoriaki Tatsumi
The document discusses performance optimization and anti-patterns when developing applications. It provides examples of common anti-patterns like inefficient filtering, unnecessary processing, and applications that do not scale well over time. Specific low-level anti-patterns are also described for databases, user interfaces, and application code. Solutions and best practices are suggested to avoid these anti-patterns and improve performance.
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
This document discusses various business intelligence tools for data analysis including ETL, OLAP, reporting, and metadata tools. It provides evaluation criteria for selecting tools, such as considering budget, requirements, and technical skills. Popular tools are identified for each category, including Informatica, Cognos, and Oracle Warehouse Builder. Implementation requires determining sources, data volume, and transformations for ETL as well as performance needs and customization for OLAP and reporting.
The document outlines the topics covered in a training course on SAP HANA 1.0 SPS 10. It includes an overview of the architecture of SAP HANA including its row and column storage, modeling in SAP HANA including creating different types of views and best practices. Administration topics like backup/recovery, user management and security are also summarized. The course further describes reporting tools that can connect to SAP HANA and loading of data from different sources into HANA. It also discusses SAP solutions that use HANA like COPA, BW and S4HANA.
InfoPath has limitations for advanced forms that require repeating sections, reporting on data, and integrating with external databases. Moving data to a SQL database addresses these issues and enables enterprise-level functionality like centralized data storage, improved reporting, and faster queries. A data-driven web service approach allows InfoPath forms to securely submit and query data from any SQL database without requiring custom code or changes to the database schema. All form templates can leverage a single shared service.
Integrating Customized Reports, Dashboards & Analysis into Your ApplicationMia Yuan Cao
Learn how to enhance your applications and products with seamlessly embedded reporting, dashboards, and analysis capabilities. Customize reports and dashboards to blend into your application's UI, as well as integrate JReport's reporting engine into your backend application server, security, and admin systems. Join us to see how your business users can create sophisticated ad hoc dashboards and reports, all on their own.
Webinar & Demo Highlights:
- Easy creation of reports and dashboards with advanced data visualization
- Embedding strategies for a wide-range of deployments
- Multi-dimensional view of your data with Visual Analysis
Embedded BI Advanced Data Visualization and Analysis into Any ApplicationJReport
This document discusses Jinfonet's JReport, an embeddable data visualization and analysis platform. JReport allows embedding advanced reporting, dashboards, and ad hoc analysis capabilities into applications. It offers high performance in-memory cubes, server clustering, and low learning curves. JReport can be embedded into on-premises or cloud-based applications and has various licensing models. It supports connecting to different data sources and empowering various user roles from application developers to business users.
This document outlines the course content for Embedded BPC, which includes an overview of Embedded BPC, basics of SAP BW4HANA, modeling objects in BWMT, loading and querying master and transaction data, planning with BPC Embedded, configuring advanced data store objects and planning functions, creating queries, analysis in Excel and Analysis for Office, advanced planning features like characteristic relations and data slices, designing planning sequences and top-down distribution, building FOX formulas, using the BPC web client, modeling environments and maintaining BW master data, process control, and transporting objects.
This document provides a summary of an individual's work experience from 2004-2006 as a project manager and system designer at GENESIIS Software. It lists 18 projects they worked on in areas including GIS, drought monitoring, irrigation mapping, and various web applications. Their responsibilities included project management, system design, development, implementation, and client coordination. Skills included languages like C/C++, Java, ASP.NET, and tools like SQL Server, PHP, and ESRI mapping products.
Nishant Kumar is an ABAP consultant with over 10 years of experience in SAP modules including FI, CO, MM, and SD. He has extensive skills in ABAP/4 programming, reporting, interfaces, and custom development. Some of his projects include developing interactive ALV reports, classical reports, BDC programs, BADI implementations, and more. He is proficient in OOABAP, performance tuning, and has worked on an SAP ECC 6.0 project for residential quarter management.
The document discusses SAP HANA, an in-memory database from SAP. It provides an overview of SAP HANA, including its introduction, hardware innovations to address bottlenecks, data storage approaches, scenarios for using SAP HANA, and licensing and implementation landscape considerations. The document also mentions data provisioning methods for SAP HANA like data replication, uploading files, and using SAP BODS (Business Objects Data Services).
Nishant Kumar has over 10 years of experience working as an ABAP consultant. He has extensive skills in ABAP/4 programming, reporting, interfaces, and dialog programming. Some projects he has worked on include developing interactive ALV reports, module pool programs, and BDC programs for uploading master data. He has also created custom BADIs and BAPIs. Nishant holds a Bachelor's degree in computer science and has a strong foundation in OOABAP, databases, and SAP modules.
This document outlines the content of a course on BIG DATA & HADOOP. The course is intended for developers and data warehouse professionals and will provide both lectures and hands-on experience working with Hadoop. It will cover key concepts like HDFS, MapReduce, YARN, and popular Hadoop ecosystems including Hive, Pig, HBase, Spark, and Impala. Lectures will be complemented by assignments and exercises working directly in a local virtual Hadoop cluster to gain experience administering, configuring, and troubleshooting Hadoop systems.
The document discusses OLAP (Online Analytical Processing) and summarizes key concepts. It describes different types of OLAP including MOLAP, ROLAP, and HOLAP and compares their functions and advantages/disadvantages. It also discusses OLAP operations like selection, roll-up, drill-down, and drill-across. The document concludes by discussing applications of OLAP and future directions such as integrating OLAP with data mining and improving security and user interaction.
OLAP provides multidimensional analysis of large datasets to help solve business problems. It uses a multidimensional data model to allow for drilling down and across different dimensions like students, exams, departments, and colleges. OLAP tools are classified as MOLAP, ROLAP, or HOLAP based on how they store and access multidimensional data. MOLAP uses a multidimensional database for fast performance while ROLAP accesses relational databases through metadata. HOLAP provides some analysis directly on relational data or through intermediate MOLAP storage. Web-enabled OLAP allows interactive querying over the internet.
Pr dc 2015 sql server is cheaper than open sourceTerry Bunio
SQL Server was found to be cheaper than open source options for a data warehouse project with the following requirements:
- Serve 100% operational reports from 1TB of data
- No need for advanced features like big data support
- Requirement was for basic textual reporting
An investigation was conducted of SQL Server, Oracle, Sybase, MySQL, and PostgreSQL. SQL Server and PostgreSQL were evaluated further based on costs and functionality. After a 10 year total cost of ownership analysis, SQL Server was found to be cheaper despite having a higher initial license cost. The lessons learned were that open source options are not always cheaper, to test options yourself rather than rely on biased reports, and that Oracle is very expensive.
This document provides tips for optimizing performance in Power BI by focusing on different areas like data sources, the data model, visuals, dashboards, and using trace and log files. Some key recommendations include filtering data early, keeping the data model and queries simple, limiting visual complexity, monitoring resource usage, and leveraging log files to identify specific waits and bottlenecks. An overall approach of focusing on time-based optimization by identifying and addressing the areas contributing most to latency is advocated.
This presentation explains the three layer API design which organisations can use to get most out of there systems with less development and maintenance time spent on fixing issues as a whole in org.
The document discusses the architecture of Oracle Business Intelligence (OBIEE). It describes the key components including the BI Server, BI Scheduler, Repository, and data sources. It explains how queries from clients are processed, cached, and passed to underlying databases. It also covers security approaches like data level security and object level security. Repository (.rpd) files are described as containing all metadata and security rules to define OBIEE solutions. Approaches to OLAP like ROLAP, MOLAP, and hybrid OLAP are also summarized.
Blackboard DevCon 2011 - Developing B2 for Performance and ScalabilityNoriaki Tatsumi
The document discusses performance optimization and anti-patterns when developing applications. It provides examples of common anti-patterns like inefficient filtering, unnecessary processing, and applications that do not scale well over time. Specific low-level anti-patterns are also described for databases, user interfaces, and application code. Solutions and best practices are suggested to avoid these anti-patterns and improve performance.
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
This document discusses various business intelligence tools for data analysis including ETL, OLAP, reporting, and metadata tools. It provides evaluation criteria for selecting tools, such as considering budget, requirements, and technical skills. Popular tools are identified for each category, including Informatica, Cognos, and Oracle Warehouse Builder. Implementation requires determining sources, data volume, and transformations for ETL as well as performance needs and customization for OLAP and reporting.
Framing the Argument: How to Scale Faster with NoSQLInside Analysis
The Briefing Room with Dr. Robin Bloor and IBM Cloudant
Live Webcast March 24, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e8bf62408d47e76c43aa73be08377e41c
Context matters. Perspective matters. Thinking outside the box? That's often the key! While the Structured Query Language remains the lingua Franca of data, there are some views of the world that are best rendered with the benefit of NoSQL engines. As usual, that's easier said than done. How can your organization migrate from a structured query to unstructured or semi-structured query language?
Register for this episode of The Briefing Room to find out! Veteran Analyst Dr. Robin Bloor will provide a detailed assessment of serious considerations when using NoSQL engines in conjunction with SQL. He'll be briefed by Ryan Millay of IBM Cloudant, who will showcase his company's solution, and how it's addressing the more vexing challenges facing today's information managers.
Visit InsideAnalysis.com for more information.
1) ERP systems integrate various business functions and processes through a shared database. This provides seamless information flow across the organization.
2) Implementation risks include choosing the wrong ERP, high costs and cost overruns, and disruptions to operations during business process reengineering.
3) Internal controls and auditing are impacted through increased reliance on programmed controls versus manual intervention, issues with segregating duties in an integrated system, and ensuring appropriate access controls over the ERP system and data.
A BASILar Approach for Building Web APIs on top of SPARQL EndpointsEnrico Daga
Presented at #SALAD2015
The heterogeneity of methods and technologies to publish open data is still an issue to develop distributed systems on the Web. On the one hand, Web APIs, the most popular approach to offer data services, implement REST principles, which focus on addressing loose coupling and interoperability issues. On the other hand, Linked Data, available through SPARQL endpoints, focus on data integration between distributed data sources. We proposes BASIL, an approach to build Web APIs on top of SPARQL endpoints, in order to benefit of the advantages from both Web APIs and Linked Data approaches. Compared to similar solution, BASIL aims on minimising the learning curve for users to promote its adoption. The main feature of BASIL is a simple API that does not introduce new specifications, formalisms and technologies for users that belong to both Web APIs and Linked Data communities.
This document discusses strategies for managing large data volumes in Salesforce, including:
- Skinny tables, which combine standard and custom fields to improve performance.
- Indexing principles and best practices for queries.
- Considerations for divisions, mashups, ownership skew, and parenting skew.
- A multi-step data load strategy involving preparation, execution, and post-load configuration.
- Archiving techniques like using middleware, Heroku, or Big Objects to improve performance by limiting data in Salesforce.
Office 365 Power Tools: What to use When? Forms, Flows, PowerApps, PowerBIJoel Oleson
Apps, Forms, Workflows and Tools of Office 365: What to Use When?
Forms - Survey, Quizzes, Polls
Flow - Triggers, Actions, Conditions
PowerApps - Web UX, Mobile UX, Admin Center, Canvas Apps
PowerBI - Visualizations, Datasets, Dashboards and Reports
When does Flow go premium? for SalesForce and Oracle...
When to use Plan 1, Plan 2
What about PowerBI Premium vs Pro
From Insights to Value - Building a Modern Logical Data Lake to Drive User Ad...DataWorks Summit
Businesses often have to interact with different data sources to get a unified view of the business or to resolve discrepancies. These EDW data repositories are often large and complex, are business critical, and cannot afford downtime. This session will share best practices and lessons learned for building a Data Fabric on Spark / Hadoop / HIVE/ NoSQL that provides a unified view, enables a simplified access to the data repositories, resolves technical challenges and adds business value. Businesses often have to interact with different data sources to get a unified view of the business or to resolve discrepancies. These EDW data repositories are often large and complex, are business critical, and cannot afford downtime. This session will share best practices and lessons learned for building a Data Fabric on Spark / Hadoop / HIVE/ NoSQL that provides a unified view, enables a simplified access to the data repositories, resolves technical challenges and adds business value.
Optimizing Your Tableau Dashboards for Speed.
Data source optimization involves enhancing the performance and efficiency of data retrieval and integration processes. Here are five key points explaining data source optimization.
Extracts vs. Live Connections:
Consider using data extracts (hyper files) for large datasets.
Extracts are pre-aggregated and can provide faster query response times compared to live connections to databases.
Data Source Filters:
Apply data source filters to limit the data retrieved.
Filters reduce the amount of data transferred, improving query and dashboard performance.
Aggregation:
Use aggregation functions (e.g., SUM, AVG) at the data source level.
Aggregating data in the source reduces the amount of data transmitted to Tableau, improving query speed.
Optimized Queries:
Craft optimized SQL queries when connecting to relational databases.
Well-optimized queries fetch only the necessary data, minimizing query execution time.
Incremental Updates:
Implement incremental data updates when possible.
Incremental updates add only new or modified data, reducing the volume of data transferred during refreshes.
Neo4j GraphDay Seattle- Sept19- in the enterpriseNeo4j
The document discusses Neo4j's graph database platform and features. It highlights Neo4j's native graph processing capabilities, Cypher query language, and enterprise editions that provide high availability, causal clustering, and multi-data center support. The document also discusses Neo4j's performance advantages over relational and other NoSQL databases for connected data through its index-free adjacency and in-memory architecture.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
1. Pros and cons of using Tableau with
OLAP and RDB
Balaji M
2. 2 Confidential Services
Simplified Tableau Dashboard Creation
Users do not have the expertise to build database relationships or join
database tables from a back end database.
• OLAP
– Data relationships already exist
– Defined hierarchies and metrics exist
• RDB
– User needs to re-create data relationships
– All metrics and formulas need to be re-created
– User needs much more technical expertise
• Risk
If users are given the opportunity to create their own data relationships and metrics,
they could become confused, build the wrong data relationships, and ultimately lose
confidence in, and not use. the system.
3. 3 Confidential Services
Fast Data Query Performance
When comparing the performance of OLAP vs. RDB, it is clear that OLAP outperforms a RDB when querying data.
OLAP
• Created specifically to aggregate data
• Hierarchical data structures well-defined
• Data retrieval performance exceeds that of a RDB
RDB
• Fact tables can contain millions of rows which is time-consuming for a RDB to aggregate
• User needs to understand table relationships to get proper performance gains
Risk
• Users do not understand the design of the RDB warehouse. If the users are given access to build their own relationships, then it is possible for the queries
to run poorly due to invalid associations. The DI development team has typically spent many weeks or months building a fast and robust OLAP design. The
performance of the OLAP cube will exceed querying directly against the database.
4. 4 Confidential Services
Summary
If your reports has built an OLAP cube for reporting, Tableau should be
sourced from the OLAP cube rather than a relational database source due to
query performance, less user knowledge required, reusability of the OLAP
components across multiple interfaces, and the maintainability of hierarchies,
metrics and calculations.
5. 5 Confidential Services
Few Limitations of Tableau
• Drill down analysis are limited
• Role based security access is not at different level
• Pixel perfect reporting
Videos:
• Analytics Anywhere: The Power of Tableau Mobile:
• https://www.youtube.com/watch?v=3pNvBK_VPs0
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• Tableau and SalesForce Unlock the power:
• https://www.youtube.com/watch?v=9YoGCy2-7uY