Data mapping in an important part of every data process. This eBook will help you understand what is data mapping and how it can help you establish connection between disparate data sets.
Building robust CDC pipeline with Apache Hudi and DebeziumTathastu.ai
We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source community.
Created at the University of Berkeley in California, Apache Spark combines a distributed computing system through computer clusters with a simple and elegant way of writing programs. Spark is considered the first open source software that makes distribution programming really accessible to data scientists. Here you can find an introduction and basic concepts.
The document discusses Snowflake, a cloud data platform. It covers Snowflake's data landscape and benefits over legacy systems. It also describes how Snowflake can be deployed on AWS, Azure and GCP. Pricing is noted to vary by region but not cloud platform. The document outlines Snowflake's editions, architecture using a shared-nothing model, support for structured data, storage compression, and virtual warehouses that can autoscale. Security features like MFA and encryption are highlighted.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
Introducing Snowflake, an elastic data warehouse delivered as a service in the cloud. It aims to simplify data warehousing by removing the need for customers to manage infrastructure, scaling, and tuning. Snowflake uses a multi-cluster architecture to provide elastic scaling of storage, compute, and concurrency. It can bring together structured and semi-structured data for analysis without requiring data transformation. Customers have seen significant improvements in performance, cost savings, and the ability to add new workloads compared to traditional on-premises data warehousing solutions.
This document provides an introduction and overview of Neo4j and graph databases. It begins with an explanation of the limitations of relational databases in modeling relationships and includes slides on Neo4j's native graph data model and architecture. Additional slides cover Neo4j use cases, modeling with graphs, the Neo4j platform and features like the cloud, drivers, and visualization tools. The document concludes with examples of recommender systems queries in Cypher.
Apache Atlas provides centralized metadata services and cross-component dataset lineage tracking for Hadoop components. It aims to enable transparent, reproducible, auditable and consistent data governance across structured, unstructured, and traditional database systems. The near term roadmap includes dynamic access policy driven by metadata and enhanced Hive integration. Apache Atlas also pursues metadata exchange with non-Hadoop systems and third party vendors through REST APIs and custom reporters.
Building robust CDC pipeline with Apache Hudi and DebeziumTathastu.ai
We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source community.
Created at the University of Berkeley in California, Apache Spark combines a distributed computing system through computer clusters with a simple and elegant way of writing programs. Spark is considered the first open source software that makes distribution programming really accessible to data scientists. Here you can find an introduction and basic concepts.
The document discusses Snowflake, a cloud data platform. It covers Snowflake's data landscape and benefits over legacy systems. It also describes how Snowflake can be deployed on AWS, Azure and GCP. Pricing is noted to vary by region but not cloud platform. The document outlines Snowflake's editions, architecture using a shared-nothing model, support for structured data, storage compression, and virtual warehouses that can autoscale. Security features like MFA and encryption are highlighted.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
Introducing Snowflake, an elastic data warehouse delivered as a service in the cloud. It aims to simplify data warehousing by removing the need for customers to manage infrastructure, scaling, and tuning. Snowflake uses a multi-cluster architecture to provide elastic scaling of storage, compute, and concurrency. It can bring together structured and semi-structured data for analysis without requiring data transformation. Customers have seen significant improvements in performance, cost savings, and the ability to add new workloads compared to traditional on-premises data warehousing solutions.
This document provides an introduction and overview of Neo4j and graph databases. It begins with an explanation of the limitations of relational databases in modeling relationships and includes slides on Neo4j's native graph data model and architecture. Additional slides cover Neo4j use cases, modeling with graphs, the Neo4j platform and features like the cloud, drivers, and visualization tools. The document concludes with examples of recommender systems queries in Cypher.
Apache Atlas provides centralized metadata services and cross-component dataset lineage tracking for Hadoop components. It aims to enable transparent, reproducible, auditable and consistent data governance across structured, unstructured, and traditional database systems. The near term roadmap includes dynamic access policy driven by metadata and enhanced Hive integration. Apache Atlas also pursues metadata exchange with non-Hadoop systems and third party vendors through REST APIs and custom reporters.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
This is the presentation for the lecture of Dimitar Mitov "Data Analytics with Dremio" (in Bulgarian), part of OpenFest 2022: https://www.openfest.org/2022/bg/full-schedule-bg/
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Vault Modeling and Methodology introduction that I provided to a Montreal event in September 2011. It covers an introduction and overview of the Data Vault components for Business Intelligence and Data Warehousing. I am Dan Linstedt, the author and inventor of Data Vault Modeling and methodology.
If you use the images anywhere in your presentations, please credit http://LearnDataVault.com as the source (me).
Thank-you kindly,
Daniel Linstedt
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Databricks and Azure to help customers accelerate innovation with one-click set up, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. As an Azure service, customers automatically benefit from the native integration with other Azure services such as Power BI, SQL Data Warehouse, and Cosmos DB, as well as from enterprise-grade Azure security, including Active Directory integration, compliance, and enterprise-grade SLAs.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
This document discusses Apache Ambari, an open source tool for managing Hadoop clusters. It describes how Ambari is used to manage a 2000 node Hadoop cluster, lessons learned, and new features in Ambari 1.6.0 like blueprints, views, and improved configuration and host management capabilities.
In Data Engineer's Lunch #54, we will discuss the data build tool, a tool for managing data transformations with config files rather than code. We will be connecting it to Apache Spark and using it to perform transformations.
Accompanying YouTube: https://youtu.be/dwZlYG6RCSY
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Polestar we hope to bring the power of data to organizations across industries helping them analyze billions of data points and data sets to provide real-time insights, and enabling them to make critical decisions to grow their business.
The document is a 20 page comparison of ETL tools. It includes an introduction, descriptions of 4 ETL tools (Pentaho Kettle, Talend, Informatica PowerCenter, Inaplex Inaport), and a section comparing the tools on various criteria such as cost, ease of use, speed and data quality. The comparison chart suggests Informatica PowerCenter is the fastest and most full-featured tool while open source options like Pentaho Kettle and Talend offer lower costs but require more manual configuration.
This document provides an introduction and overview of Apache Spark with Python (PySpark). It discusses key Spark concepts like RDDs, DataFrames, Spark SQL, Spark Streaming, GraphX, and MLlib. It includes code examples demonstrating how to work with data using PySpark for each of these concepts.
Workshop held at Open Repository 2018, Bozeman, Montana
As of late 2016, a DSpace 7 UI Working Group has begun developing an Angular User Interface which will replace the existing UIs in DSpace 7. This effort also includes the development of a new REST API for DSpace, designed to follow the principles of a RESTful webservice and adopt emerging standards and formats. The goals of the REST API are twofold: (1) to fully support the new Angular UI, and (2) to provide a rich, RESTful integration point for third-party services and tools.
This workshop will allow developers to become more familiar with the new REST API framework before DSpace 7 is released.
This hands-on developers workshop will provide attendees with an overview of the DSpace 7 REST framework:
- standards / best practices that the API is based on (HAL, JSON+PATCH, JWT)
- DSpace 7 REST Contract (documentation of all endpoints)
- interacting with the REST API (via HAL browser, curl and/or postman)
- how to build new endpoints into the REST API
- where to look when issues arise
- how to document and test existing/new endpoints
Attendees will be expected to setup a virtual machine (or install the DSpace 7 codebase locally) to get more familiar with the codebase/development tools.
Technological Geeks Video 13 :-
Video Link :- https://youtu.be/mfLxxD4vjV0
FB page Link :- https://www.facebook.com/bitwsandeep/
Contents :-
Hive Architecture
Hive Components
Limitations of Hive
Hive data model
Difference with traditional RDBMS
Type system in Hive
Azure Data Factory uses linked services to connect resources, datasets to define data structures, and pipelines containing activities to perform tasks on data. Key concepts include linked services to store connection strings, datasets that point to input/output data, data flows for visual data transformations without code, activities that take datasets as input/output, pipelines that group/manage activities, and triggers that determine when pipelines execute.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
The document provides an overview of various SSIS data flow tasks including connection managers, derived columns, aggregates, lookups, joins, unions, splits, multicasts, conversions and more. It describes what each task does, how it works, and examples of how to use it in an ETL process. The tasks are building blocks that can be combined to construct complex data integration and transformation solutions.
Apache Spark is a cluster computing framework designed for fast, general-purpose processing of large datasets. It uses in-memory computing to improve processing speeds. Spark operations include transformations that create new datasets and actions that return values. The Spark stack includes Resilient Distributed Datasets (RDDs) for fault-tolerant data sharing across a cluster. Spark Streaming processes live data streams using a discretized stream model.
Understanding Data Modelling Techniques: A Compre….pdfLynn588356
This document provides an overview of data modeling techniques. It discusses the types of data models including conceptual, logical and physical models. It also outlines some common data modeling techniques such as hierarchical, relational, entity-relationship, object-oriented and dimensional modeling. Dimensional modeling includes star and snowflake schemas. The benefits of effective data modeling are also highlighted such as improved data quality, reduced costs and quicker time to market.
Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision to LifeSG Analytics
The new data technologies, along with legacy infrastructure, are driving market-driven innovations like personalized offers, real-time alerts, and predictive maintenance. However, these technical additions - ranging from data lakes to analytics platforms to stream processing and data mesh —have increased the complexity of data architectures. They are significantly hampering the ongoing ability of an organization to deliver new capabilities while ensuring the integrity of artificial intelligence (AI) models. https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
This is the presentation for the lecture of Dimitar Mitov "Data Analytics with Dremio" (in Bulgarian), part of OpenFest 2022: https://www.openfest.org/2022/bg/full-schedule-bg/
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Vault Modeling and Methodology introduction that I provided to a Montreal event in September 2011. It covers an introduction and overview of the Data Vault components for Business Intelligence and Data Warehousing. I am Dan Linstedt, the author and inventor of Data Vault Modeling and methodology.
If you use the images anywhere in your presentations, please credit http://LearnDataVault.com as the source (me).
Thank-you kindly,
Daniel Linstedt
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Databricks and Azure to help customers accelerate innovation with one-click set up, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. As an Azure service, customers automatically benefit from the native integration with other Azure services such as Power BI, SQL Data Warehouse, and Cosmos DB, as well as from enterprise-grade Azure security, including Active Directory integration, compliance, and enterprise-grade SLAs.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
This document discusses Apache Ambari, an open source tool for managing Hadoop clusters. It describes how Ambari is used to manage a 2000 node Hadoop cluster, lessons learned, and new features in Ambari 1.6.0 like blueprints, views, and improved configuration and host management capabilities.
In Data Engineer's Lunch #54, we will discuss the data build tool, a tool for managing data transformations with config files rather than code. We will be connecting it to Apache Spark and using it to perform transformations.
Accompanying YouTube: https://youtu.be/dwZlYG6RCSY
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Polestar we hope to bring the power of data to organizations across industries helping them analyze billions of data points and data sets to provide real-time insights, and enabling them to make critical decisions to grow their business.
The document is a 20 page comparison of ETL tools. It includes an introduction, descriptions of 4 ETL tools (Pentaho Kettle, Talend, Informatica PowerCenter, Inaplex Inaport), and a section comparing the tools on various criteria such as cost, ease of use, speed and data quality. The comparison chart suggests Informatica PowerCenter is the fastest and most full-featured tool while open source options like Pentaho Kettle and Talend offer lower costs but require more manual configuration.
This document provides an introduction and overview of Apache Spark with Python (PySpark). It discusses key Spark concepts like RDDs, DataFrames, Spark SQL, Spark Streaming, GraphX, and MLlib. It includes code examples demonstrating how to work with data using PySpark for each of these concepts.
Workshop held at Open Repository 2018, Bozeman, Montana
As of late 2016, a DSpace 7 UI Working Group has begun developing an Angular User Interface which will replace the existing UIs in DSpace 7. This effort also includes the development of a new REST API for DSpace, designed to follow the principles of a RESTful webservice and adopt emerging standards and formats. The goals of the REST API are twofold: (1) to fully support the new Angular UI, and (2) to provide a rich, RESTful integration point for third-party services and tools.
This workshop will allow developers to become more familiar with the new REST API framework before DSpace 7 is released.
This hands-on developers workshop will provide attendees with an overview of the DSpace 7 REST framework:
- standards / best practices that the API is based on (HAL, JSON+PATCH, JWT)
- DSpace 7 REST Contract (documentation of all endpoints)
- interacting with the REST API (via HAL browser, curl and/or postman)
- how to build new endpoints into the REST API
- where to look when issues arise
- how to document and test existing/new endpoints
Attendees will be expected to setup a virtual machine (or install the DSpace 7 codebase locally) to get more familiar with the codebase/development tools.
Technological Geeks Video 13 :-
Video Link :- https://youtu.be/mfLxxD4vjV0
FB page Link :- https://www.facebook.com/bitwsandeep/
Contents :-
Hive Architecture
Hive Components
Limitations of Hive
Hive data model
Difference with traditional RDBMS
Type system in Hive
Azure Data Factory uses linked services to connect resources, datasets to define data structures, and pipelines containing activities to perform tasks on data. Key concepts include linked services to store connection strings, datasets that point to input/output data, data flows for visual data transformations without code, activities that take datasets as input/output, pipelines that group/manage activities, and triggers that determine when pipelines execute.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
The document provides an overview of various SSIS data flow tasks including connection managers, derived columns, aggregates, lookups, joins, unions, splits, multicasts, conversions and more. It describes what each task does, how it works, and examples of how to use it in an ETL process. The tasks are building blocks that can be combined to construct complex data integration and transformation solutions.
Apache Spark is a cluster computing framework designed for fast, general-purpose processing of large datasets. It uses in-memory computing to improve processing speeds. Spark operations include transformations that create new datasets and actions that return values. The Spark stack includes Resilient Distributed Datasets (RDDs) for fault-tolerant data sharing across a cluster. Spark Streaming processes live data streams using a discretized stream model.
Understanding Data Modelling Techniques: A Compre….pdfLynn588356
This document provides an overview of data modeling techniques. It discusses the types of data models including conceptual, logical and physical models. It also outlines some common data modeling techniques such as hierarchical, relational, entity-relationship, object-oriented and dimensional modeling. Dimensional modeling includes star and snowflake schemas. The benefits of effective data modeling are also highlighted such as improved data quality, reduced costs and quicker time to market.
Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision to LifeSG Analytics
The new data technologies, along with legacy infrastructure, are driving market-driven innovations like personalized offers, real-time alerts, and predictive maintenance. However, these technical additions - ranging from data lakes to analytics platforms to stream processing and data mesh —have increased the complexity of data architectures. They are significantly hampering the ongoing ability of an organization to deliver new capabilities while ensuring the integrity of artificial intelligence (AI) models. https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/
data collection, data integration, data management, data modeling.pptxSourabhkumar729579
it contains presentation of data collection, data integration, data management, data modeling.
it is made by sourabh kumar student of MCA from central university of haryana
The document discusses business intelligence vendors and their capabilities. It notes that the winners will be those able to quickly gather, analyze, and use data to make decisions. It also discusses how vendors are integrating different business intelligence functions into unified suites and how database vendors are building predictive analytics directly into their databases to enable real-time decision making from transactional data.
The Double win business transformation and in-year ROI and TCO reductionMongoDB
This document discusses how modern information management with flexible data platforms like MongoDB can help businesses transform and drive ROI through cost reduction and increased productivity compared to legacy systems. It provides examples of strategic areas where MongoDB can modernize an organization's full technology stack from data in motion/at rest to apps, compute, storage and networks. Success stories show how MongoDB has helped companies like Barclays reduce costs and complexity while improving resiliency, agility and innovation.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
TechoERP, which is hosted in the cloud, is especially beneficial to businesses since it gives them access to full-featured apps at a low cost without requiring a large initial investment in hardware and software. A company can rapidly scale their business productivity software using the right cloud provider as their business grows or a new company is added.
This document provides an overview of key concepts related to data warehousing including what a data warehouse is, common data warehouse architectures, types of data warehouses, and dimensional modeling techniques. It defines key terms like facts, dimensions, star schemas, and snowflake schemas and provides examples of each. It also discusses business intelligence tools that can analyze and extract insights from data warehouses.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
The document discusses how a semantic "data lake" can help organizations extract meaning and insights from large amounts of digital data. A data lake combines data from different sources and uses semantic models, tagging, and algorithms to help users more quickly find relevant data relationships and insights. It describes how semantic technology plays a key role in data ingestion, management, modeling of different views, querying, and exposing analytics as web services to create personalized customer experiences.
This document discusses using Microsoft Excel 2013 and Microsoft Access to create an offers bank decision support system (DSS). It proposes a 4 phase approach: 1) Create a database and star schema using Access, 2) Fill the database with data by defining dimensions and measures and retrieving data in Excel, 3) Create a dashboard in Excel, 4) Analyze past trends and predict future trends using data mining. The document also provides background on business intelligence solutions and reviews literature on using BI to turn raw data into meaningful business insights.
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
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For companies moving from an on-premise legacy ERP system to Microsoft’s cloud-based Dynamics 365 for Finance and Operations (“D365”), there are some unique challenges and new tools to leverage when considering the data migration activity.
Microsoft delivers the Data Management Framework (“DMF”) tool to assist customers with data migration for D365. Data migration itself consists of three distinct activities, as illustrated below: Data extraction (from legacy systems), data transformation and data import into D365. DMF will assist in the import into the new D365 application, but what is the best way to extract and transform, to “ready” the data for the import?
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Mind Mapping + Business Intelligence = freeDatamap.
Unchain your data with the lightest and most intuitive self-service BI platform. Try a new data browsing experience thanks to a holistic and organization-wide dashboard to understand all the key aspects of your business in a unified data map.
With freeDatamap, access the right data, share the knowledge, break silos, help data to go “social”, make data available and collectively enriched.
• Find your way in an overwhelming amount of information.
• Visualize your data in a centralized trusted map.
• Display your business process across your organization.
• Navigate into the map and drill down to find the root cause of an indicator.
• Find any atomic data thanks to a powerful and immediate search engine.
• Reduce time to make fact based decisions.
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Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
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4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
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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.
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2. Summary
Enterprise data is getting more dispersed and voluminous by the day, and at the same time, it has become more important
than ever for businesses to leverage data and transform it into actionable insights. However, enterprises today collect
information from an array of data points, and they may not always speak the same language.
To integrate this data and make sense of it, data mapping is used, which is the process of establishing relationships
between heterogeneous systems. As a primary step in a variety of data processes, data mapping is integral to the success
of an organization’s data initiatives.
This eBook will impart in-depth insight into the data mapping process. It will further discuss its importance in the data
integration cycle, the commonly used data mapping techniques, and how you can evaluate the best tool for your unique
data integration projects. Finally, it will illustrate how Astera Centerprise handles complex data mapping tasks to simplify
enterprise data integration projects.
3. Table of Contents
Data Mapping: The Foundation of Every Data Pipeline
THE BASICS .......................................................................................................................
What is Data Mapping?
THE PURPOSE ..................................................................................................................
Significance of Data Mapping
THE METHOD ..................................................................................................................
Data Mapping Techniques
Types of Data Mapping Tools
How to Evaluate and Select the Best Data Mapping Software
ASTERA CENTERPRISE ...................................................................................................
Simplify Complex Data Mappings
Visual Interface
Built-in Data Quality, Profiling, and Cleansing Capabilities
Out-of-the-Box Connectors
Auto-Mapping
Dynamic Layout
Instant Data Preview
CONCLUSION .................................................................................................................
04
05
06
07
09
10
11
11
13
14
14
15
15
15
16
16
17
Data Mapping: The Foundation of Every Data Pipeline
5. What is Data Mapping?
Data mapping is the process of mapping data fields
from a source file to their related target fields.
Mapping tasks vary in complexity, depending on the hierarchy of the data being mapped, as well as the disparity
between the structure of the source and the target. Every business application, whether on-premise or cloud, uses
metadata to explain the data fields and attributes that constitute the data, as well as semantic rules that govern how
data is stored within that application or repository.
For example, a company stores its data in Microsoft Dynamics CRM, which contains several data sets with
different objects, such as Leads, Opportunities, and Competitors. Each of these data sets has several fields like Name,
Account Owner, City, Country, Job Title, and more. The application also has a defined schema along with attributes,
enumerations, and mapping rules. Therefore, if a new record is to be added to the schema of a data object, a data
map needs to be created from the data source to the Microsoft Dynamics CRM account.
Depending on the number, schema, and primary
and foreign keys of the relational databases, database
mappings can have a varying degree of complexity.
Similarly, depending on the data management needs of an enterprise and capabilities of the data mapping software,
data mapping is used to accomplish a range of data integration and transformation tasks.
05Data Mapping: The Foundation of Every Data Pipeline |
6. The Purpose
Why is Data Mapping Important?
06Data Mapping: The Foundation of Every Data Pipeline |
7. Significance of Data Mapping
To leverage data and extract business value out of it, the
information collected from various external and internal
sources must be unified and transformed into a format
suitable for the operational and analytical processes.
This is accomplished through data mapping, which is an integral step in various data
management processes, including:
07Data Mapping: The Foundation of Every Data Pipeline |
8. Data Integration
Data mapping is the initial step in the integration process in which data from a source is converted into a destina-
tion-compatible format and loaded into the target location. Data mapping software can reduce or eliminate the need
for manual data entry, resulting in fewer errors and more reliable data. For successful data integration, the source and
target data repositories must have the same data model. However, it is rare for any two data repositories to have the
same schema. Data mapping tools help bridge the differences in the schemas of data source and destination, allowing
businesses to consolidate information from different data points easily.
Data Migration
Data migration is the process of moving data from one database to another. While there are various steps involved in
the process, creating mappings between source and target is one of the most challenging and time-consuming tasks,
particularly when done manually. Inaccurate and invalid mappings at this stage not only impact the accuracy and
completeness of data being migrated but can even lead to the failure of the data migration project. Therefore, using a
code-free data mapping solution that can automate the process is important to migrate data to the destination
successfully.
Data Warehousing
Data mapping in a data warehouse is the process of creating a connection between the source and target tables or
attributes. Using data mapping, businesses can build a logical data model and define how data will be structured and stored
in the data warehouse. The process begins with collecting all the required information and understanding the source data.
Once that has been done and a data mapping document created, building the transformation rules and creating mappings
is a simple process with a data mapping solution.
Data Transformation
Because enterprise data resides in a variety of locations and formats, data transformation is essential to break information
silos and draw insights. Data mapping is the first step in data transformation. It is done to create a framework of what
changes will be made to data before it is loaded into the target database.
Electronic Data Interchange
Data mapping plays a significant role in EDI file conversion by converting the files into various formats, such as XML,
JSON, and Excel. An intuitive data mapping tool allows the user to extract data from different sources and utilize built-in
transformations and functions to map data to EDI formats without writing a single line of code. This helps perform
seamless B2B data exchange.
08Data Mapping: The Foundation of Every Data Pipeline |
9. The Method
Finding the Right Tools and Techniques
09Data Mapping: The Foundation of Every Data Pipeline |
10. Data Mapping Techniques
Based on the level of automation, data mapping techniques can be divided into three types:
1. Manual Data Mapping
Manual data mapping involves hand-coding the mappings between the source and target data systems. Although
hand-coded, the manual data mapping process offers unlimited flexibility for unique mapping scenarios initially.
However, it can become challenging to maintain and scale as the mapping needs of the business grow complex.
2. Semi-Automated Data Mapping
Manual data mapping involves hand-coding the mappings between the source and target data systems. Although
hand-coded, the manual data mapping process offers unlimited flexibility for unique mapping scenarios initially.
However, it can become challenging to maintain and scale as the mapping needs of the business grow complex.
Once schema mapping has been done, Java, C++, or C# code is generated to achieve the required data conversion
tasks. The programming language used may vary depending on the data mapping tool used.
3. Semi-Automated Data Mapping
Automated data mapping tools feature a complete code-free environment for data mapping tasks of any complexity.
Mappings are created between the source and target objects in a simple drag-and-drop manner. An automated data
mapping tool also has built-in transformations to convert data from XML to JSON, EDI to XML, XML to XLS, hierarchical
to flat files, or any format without writing a single line of code.
Database 1 Database 2
Student Name
ID
Level
Major
Marks
Name
SSN
Major
Grades
Demonstrating the schemas of Database 1 and Database 2
10Data Mapping: The Foundation of Every Data Pipeline |
11. How to Evaluate and Select the
Best Data Mapping Software
Selecting a data mapping tool that’s the best fit for the enterprise is critical to the success of any data integration
project. The process involves identifying the unique data mapping requirements of the business and
must-have features.
Online reviews on websites like Capterra, G2 Crowd, and
Software Advice can be a good starting point to shortlist
data mapping software that offers the maximum number
of features. The next step would be to classify the
features of data mapping tools into three different
categories, including must-haves, good-to-haves, and
will-not-use, depending on the unique data
management needs of the business.
Some of the key features that a data mapping
solution must have include:
Types of Data Mapping Tools
Data mapping tools can be divided into three broad types:
The key to
choosing the right
data mapping
software is
research.
On-Premise Cloud-Based Open-Source
Such tools are hosted on a
company’s server and native
computing infrastructure.
Many on-premise data
mapping tools eliminate the
need for hand-coding to
create complex mappings
and automate repetitive tasks
in the data mapping process.
These tools leverage cloud
technology to help a business
perform its data mapping
projects.
Open-source mapping tools
provide a low-cost alternative
to on-premise data mapping
solutions.These tools work
better for small businesses
with lower data volumes and
simpler use-cases.
11Data Mapping: The Foundation of Every Data Pipeline |
12. Support for various databases, and hierarchical and flat file formats, such as delimited, XML, JSON, EDI, Excel, and text files are
the basic staples of all data mapping tools. In addition, for businesses that need to integrate structured data with semi-struc-
tured and unstructured data sources, support for PDF, PDF forms, RTF, weblogs, etc. is also a key feature.
If your business uses a cloud-based CRM application, such as Salesforce or Microsoft Dynamics CRM, look for a data mapping
tool that offers out-of-the-box connectivity to these enterprise applications.
To break down information silos and allow both data professionals and business users access to enterprise data, it is import-
ant to select a data mapping solution that offers you a code-free way to create data maps. From built-in transformations to
join, filter, and sort data to a range of expressions and functions, user-friendly data mapping tools feature an extensive library
of transformations to fulfill the data conversion needs of an enterprise.
Since data mapping jobs, if not automated, can take up a significant amount of developer resources and time, opting for data
mapping software with process orchestration capabilities can bring cost-savings to a business. With the ability to orchestrate a
complete workflow, and time-based and event-triggered job scheduling, these solutions automate data mapping and transfor-
mation process, thereby delivering analytics-ready data faster.
Mapping data to and from formats such as JSON, XML, and EDI can be complex due to the diversity in data structures. Howev-
er, to prevent mapping errors at the design-time, an effective data mapping tool should feature a real-time testing engine that
lets the user view the processed and raw data at any step of the data integration process.
Support for a Diverse Set of Source Systems
Graphical, Drag-and-Drop, Code-Free User Interface
Ability to Schedule and Automate Mapping Jobs
Real-Time Testing and Validation of Mappings
12Data Mapping: The Foundation of Every Data Pipeline |
13. Astera Centerprise
Execute Data Mapping Jobs in a
Code-Free Environment
13Data Mapping: The Foundation of Every Data Pipeline |
14. Simplify Complex Data Mappings
with Astera Centerprise
Data from business partners and other third parties, as well as internal departments, can arrive in a myriad of formats
that needs to be mapped to a unified system.
Astera Centerprise is a powerful integration solution that
supports all types of data mappings. In addition, it also
contains built-in data quality, profiling, and automation
capabilities in a single, familiar drag-and-drop, visual
environment.
Astera Centerprise’s impressive complex data mapping capabilities make it an easy-to-use platform for overcoming the
challenges of complex hierarchical structures such as XML, electronic data interchange (EDI), web services, and more.
Here are a few other features that simplify data mapping tasks in Astera Centerprise:
Visual Interface
To carry out a successful data process, it’s essential to correctly map data from source to destination. To enable business
personnel and data professionals to use these processes easily, Astera Centerprise offers enhanced functionality to
develop, debug, and test mappings in a visual environment, without writing a single line of code.
Intuitive and code-free UI
14Data Mapping: The Foundation of Every Data Pipeline |
15. Built-in Data Quality, Profiling, and Cleansing Capabilities
With Astera Centerprise’s pre-built data profiling feature, you can analyze your data at any point in the dataflow, and find
out about its structure, quality, and accuracy. Furthermore, you can add data quality rules to validate records and identify
inaccuracies, and correct them through data cleanse transformation.
This ensures that accurate and high-quality data goes into your data pipeline.
A simple dataflow with built-in data profile, cleanse, and quality transformations
Out-of-the-Box Connectors
The solution has a library of built-in connectors that seamlessly connects with disparate data structures, such as XML, JSON,
EDI, etc. Whether you require connectivity to business applications (Microsoft Dynamics CRM, Salesforce, etc.), databases
(SQL Server, IBM DB2, Teradata) or file formats (Excel, PDF), Astera Centerprise can integrate these data sources through
drag-and-drop mapping.
Auto-Mapping
The challenges of handling variation in data collected from third-party applications, and ensuring consistency between
internal and external data are handled through the SmartMatch functionality in Astera Centerprise.
This feature provides an intuitive and scalable method of resolving naming conflicts and inconsistencies that arise during
high-volume data integrations. It allows users to create a Synonym Dictionary File that contains current and alternative values
that may appear in the header field of an input table. Centerprise will then automatically match irregular headers to the
correct column at run-time and extract data from them as normal.
15Data Mapping: The Foundation of Every Data Pipeline |
16. Creating Synonym Dictionary File to leverage SmartMatch functionality
Astera Centerprise features a revolutionary Instant Data Preview engine that lets developers preview the output of their
data mapping project at any step with a single click. There’s no need to execute a dataflow to have visibility into the
expected result of your mapping. Instead, Centerprise enables real-time testing and validation of mappings by allowing
users to preview a sample or all of the data as it is being transformed, thereby improving iteration time and providing a
shorter feedback cycle for developers working on complex data mapping projects.
Dynamic Layout
The Dynamic Layout feature in Astera
Centerprise streamlines time-consuming
integration tasks with intuitive features that allow
parameter configuration for source and
destination entities with all changes
automatically propagated throughout linked data
maps. These changes are initiated based on the
pre-defined paths and relationships within the
dataflows and workflows, regardless of the
visible structure of source entities.
With Dynamic Layout enabled, these differentials
can be automatically identified and implemented
in your ETL and ELT processes without any
disruptions.
Instant Data Preview
Enabling the Dynamic Layout option
16Data Mapping: The Foundation of Every Data Pipeline |
17. Conclusion
Data mapping, transformation, and integration can be extremely tedious and demanding. Even a simple task such as
reading a CSV file into a list of class instances can require a large amount of coding because, while most tasks share
much in common, they are each just different enough to require their own data conversion methods.
Enterprise-grade tools, like Astera Centerprise, simplify complex data mapping tasks through a wide range of
user-friendly features. This results in a well-designed ETL process that is tested, validated, and optimized for
improved performance.
Astera Centerprise’s advanced data mapping functionality can ensure smooth execution of your data processes,
facilitating quick data analysis and robust decision-making for organizations.
17Data Mapping: The Foundation of Every Data Pipeline |