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.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
Data Lakehouse, Data Mesh, and Data Fabric (r1)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 data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
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.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Design Guidelines for Data Mesh and Decentralized Data OrganizationsDenodo
Watch full webinar here: https://bit.ly/3Ek4gUb
In recent years, there has been a significant push towards decentralized data organizations where different domains are partially or fully responsible for exposing their own data for analytics.
Join us in this session with Daniel Tenreiro, Sales Engineer at Denodo, in which he will share important design guidelines and best practices that can be used to implement many of the decentralization principles, such as the ones defined by the popular data mesh paradigm, using the Denodo Platform, powered by data virtualization.
Watch On-Demand & Learn:
- Overview of decentralized data organizations features
- Implementation best practices using data virtualization
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
Data Lakehouse, Data Mesh, and Data Fabric (r1)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 data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
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.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Design Guidelines for Data Mesh and Decentralized Data OrganizationsDenodo
Watch full webinar here: https://bit.ly/3Ek4gUb
In recent years, there has been a significant push towards decentralized data organizations where different domains are partially or fully responsible for exposing their own data for analytics.
Join us in this session with Daniel Tenreiro, Sales Engineer at Denodo, in which he will share important design guidelines and best practices that can be used to implement many of the decentralization principles, such as the ones defined by the popular data mesh paradigm, using the Denodo Platform, powered by data virtualization.
Watch On-Demand & Learn:
- Overview of decentralized data organizations features
- Implementation best practices using data virtualization
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Uma introdução à malha de dados e as motivações por trás dela: os modos de falhas de paradigmas anteriores de gerenciamento de big data. A proposta de Zhamak Dehghani é comparar e contrastar a malha de dados com as abordagens existentes de gerenciamento de big data, apresentando os componentes técnicos que sustentam a arquitetura de software.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
this is part 3 of the series on Data Mesh ... looking at the intersection of microservices architecture concepts, data integration / replication technologies and log-based stream integration techniques. This webinar was mostly a demonstration, but several slides used to setup the demo are included here as a PDF for viewers.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
Power BI Governance - Access Management, Recommendations and Best PracticesLearning SharePoint
This document outlines permissions management for Power BI Workspace and features of new admin, Member and Contributor Roles. Recommendations and best practices for sharing report are also included. Free to Download.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Uma introdução à malha de dados e as motivações por trás dela: os modos de falhas de paradigmas anteriores de gerenciamento de big data. A proposta de Zhamak Dehghani é comparar e contrastar a malha de dados com as abordagens existentes de gerenciamento de big data, apresentando os componentes técnicos que sustentam a arquitetura de software.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
this is part 3 of the series on Data Mesh ... looking at the intersection of microservices architecture concepts, data integration / replication technologies and log-based stream integration techniques. This webinar was mostly a demonstration, but several slides used to setup the demo are included here as a PDF for viewers.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
Power BI Governance - Access Management, Recommendations and Best PracticesLearning SharePoint
This document outlines permissions management for Power BI Workspace and features of new admin, Member and Contributor Roles. Recommendations and best practices for sharing report are also included. Free to Download.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
Enterprise Data Lake:
How to Conquer the Data Deluge and Derive Insights
that Matters
Data can be traced from various consumer sources.
Managing data is one of the most serious challenges faced
by organizations today. Organizations are adopting the data
lake models because lakes provide raw data that users can
use for data experimentation and advanced analytics.
A data lake could be a merging point of new and historic
data, thereby drawing correlations across all data using
advanced analytics. A data lake can support the self-service
data practices. This can tap undiscovered business value
from various new as well as existing data sources.
Furthermore, a data lake can aid data warehousing,
analytics, data integration by modernizing. However, lakes
also face hindrances like immature governance, user skills
and security.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
Using Data Lakes to Sail Through Your Sales GoalsIrshadKhan682442
Using Data Lakes to Sail Through Your Sales Goals Most Popular Busting 5 Common CRM Myths Fail-Proof Ways to Hire A-Lister in Sales Our Recommendations Retail Redefined - Where does the innovation takes us?
To know more visit here: https://www.denave.com/resources/ebooks/using-data-lakes-to-sail-through-your-sales-goals/
The volume, variety, velocity and veracity of big data are getting increasingly complex
each passing day. The way the data is stored, processed, managed and shared with
decision-makers is getting impacted by this complexity and to tackle the same, a
revolutionary approach to data management has come into picture. A data lake.
Busting 5 Common CRM Myths Most Read Fail-Proof Ways to Hire A-Listers in Sales Fail-Proof Ways to Use Data Lakes to Achieve Your Sales Goals Recommendations from Us Where does innovation lead us with respect to retail redefined?
WHAT IS A DATA LAKE? Know DATA LAKES & SALES ECOSYSTEMRajaraj64
As the name suggests, data lake is a large reservoir of data – structured or unstructured, fed through disparate channels. The data is fed through channels in anad-hoc manner into these data lakes, however, owing to the predefined set of rules orschema, correlation between the database is established automatically to help with the extraction of meaningful information.
For more information visit:- https://bit.ly/3lMLD1h
Data Lake v Data Warehouse
Do you know the difference?
Data lakes and data warehouses are both storage systems for big data, but they have several key differences.
A data lake is designed to store raw data of all types, including structured, semi-structured, and unstructured data. It’s a great option for companies that benefit from raw data for machine learning.
A data warehouse is designed to be a repository for already structured data to be queried and analysed for very specific purposes. It’s a better fit for companies whose business analysts need to decipher analytics in a structured system.
Understanding these key differences is important for any aspiring data professional
https://www.selectdistinct.co.uk/2024/01/02/difference-between-a-data-lake-and-a-data-warehouse/
#datawarehouse #datalake #dataanalytics
Top 60+ Data Warehouse Interview Questions and Answers.pdfDatacademy.ai
This is a comprehensive guide to the most frequently asked data warehouse interview questions and answers. It covers a wide range of topics including data warehousing concepts, ETL processes, dimensional modeling, data storage, and more. The guide aims to assist job seekers, students, and professionals in preparing for data warehouse job interviews and exams.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
We live in a world that is heavily dependent on technology. With the increased dependency on technology, the dependency on data has also increased. In the realm of data-driven decision making, the role of big data has transformed the landscape of data storage and analysis.
Got data?… now what? An introduction to modern data platformsJamesAnderson599331
What are Data Analytics Platforms? What decision points are necessary in creating a modern, unified analytics data platform? What benefits are there to building your analytics data platform on Google Cloud Platform? Susan Pierce walks us through it all.
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
In this paper, Impetus focuses at why organizations need to design an Enterprise Data Warehouse (EDW) to support the business analytics derived from the Big Data.
[DSC Europe 23] Milos Solujic - Data Lakehouse Revolutionizing Data Managemen...DataScienceConferenc1
We will dive into modern data management approaches that have become prevalent and popular across many industries, built on top of good old data lakes: Lakehouse. Here are some of the most common problems that are being solved with this novel approach: Data Silos Demolished: Discover how organizations are breaking down data silos that have plagued them for decades, unifying structured and unstructured data from diverse sources. Inefficient Data Processing: We'll unveil real-world examples of how inefficient data processing can grind productivity to a halt and explore how Data Lakehouses provide a powerful solution while improving governance and security. Real-time Analytics: Learn how modern businesses are striving to achieve real-time analytics and the role Data Lakehouses play in achieving this. Have one data copy that will serve BI, Reporting, and ML workloads
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional Business Intelligence (BI) systems provide various levels and kinds of analyses on structured data but they are not designed to handle unstructured data.
For these systems Big Data brings big problems because the data that flows in may be either structured or unstructured. That makes them hugely limited when it comes to delivering Big Data benefits.
The way forward is a complete rethink of the way we use BI - in terms of how the data is ingested, stored and analyzed.
More information: http://www.capgemini.com/big-data-analytics/pivotal
Global capability centers driving innovation growth in digital worldPolestarsolutions
Global capability centers (GCCs) are playing a crucial role in driving innovation and growth in the digital world by leveraging technology and talent.
link: https://www.polestarllp.com/ebook/global-capability-centers-driving-innovation-growth-in-digital-world
Data democratization the key to future proofing data culturePolestarsolutions
Learn how to empower your organization with accessible data insights through democratizing your data. This guide offers tips for choosing the right tools and fostering a data-driven culture.
Understand the importance of visualization and analytics for CPG Industry and know the components of building the best dashboards to help leverage the CPG value chain.
You've probably heard about Clickstream Analysis by now as it's a term which is increasingly being used. But what does it actually mean and what makes this so interesting?
Manufacturing is a sophisticated function where people try to juggle between the tasks of increasing productivity, managing their inventory, and optimizing their resource utilization. All this has to be done without compromising on the quality of the product and this is why we are talking about - Production Planning and Control strategy. This strategy for manufacturing combines two essential components of manufacturing - #productionplanning and production control.
Read our e-book to know, what the crucial stages in production planning are, some best practices, and how it is important to get a granular understanding of which section needs to do what, along with where, when, and how.
Production Planning Manufacturing inventory E-book
Link: https://www.polestarllp.com/ebook/production-planning-in-manufacturing-industry-e-book
Follow Polestar Solutions for more such content.
It is imperative for organizations to manage the flow of data at every stage of the data life cycle. In this #creative we are breaking down the data lifecycle with extended stages that have cropped up with modern practices and platforms.
Link: https://polestarllp.com/services/us/analytics-support
Augmented analytics will push the analytics adoptionPolestarsolutions
The world of data analytics is no longer restricted to data scientists, IT, and analysts. Augmented analytics combines the best aspects of ML and human curiosity to assist users get quicker insights, consider data from unique angles, increase productivity and assist users of all skill levels to make smarter decisions based on AI analytics.
Strategic workforce planning is important for companies, since employees are inarguably the most vital "asset". Today, the emergence of data analytics and planning platforms are making it easier for companies to gain insight into important metrics, answer pressing questions and to design an effective human capital management programme.
Read more - https://www.polestarllp.com/strategic-workforce-planning-for-human-capital-management
Here's Our Take On The Qlik Sense February Product Updates.
Disclaimer: (The views expressed here are of Polestar Solutions, and may not reflect those of Qlik)
With the February release, Qlik has invested intensively in enhancing the end-user as well as developer experience.
The key idea behind this upgrade has been ease of development with more focus on analytics - simplifying reporting with new and better visualizations including statistical analysis.
More info: https://www.polestarllp.com/qlik-sense-feb-2021-release
It is essential to understand the potential of this fourth industrial revolution. Take a look at the four design principles in Industry 4.0 supporting organizations in identifying and implementing Industry 4.0 scenarios.
When employees at an industrial site returned to the workplace after it was closed during the COVID-19 pandemic, they noticed a few differences.
Sensors or RFID tags were used to determine whether employees were washing their hands regularly. Computer vision determined if employees were complying with mask protocol and speakers were used to warn people of protocol violations.
What’s more, this behavioral data was collected and analyzed by the organizations to influence how people behaved at work.
Such digital accelerations are among Gartner's strategic technology trends that will enable the plasticity or flexibility that resilient businesses require in the significant upheaval driven by COVID-19 and the current economic state of the world.
Follow #PolestarSolutions for more such content.
On this Wildlife Conservation Day, let's take a pledge to do our bit to save the wildlife and their habitat against the crimes & our selfish acts to restore the natural ecosystem.
#wildlife #conservation #wildlifeConservation #WildlifeConservationDay #environment #savetheplanet
Tennis has never been entirely a number-crunching game, players winning lesser points may walk away with the match. Then, there are different surfaces and so varies the players' expertise.
But it is on a slow but steady rise.
There are aspects where #DataAnalytics is helping every party involved – Players, Coaches & Audience and this has led to the institutionalization of big data and Analytics Services.
Majority of top 20 players on ATP Tour and WTA side are leveraging it to their advantage.
This guide explores the different dimensions it is adding to the sport.
𝐓𝐚𝐤𝐞 𝐚 𝐭𝐨𝐮𝐫: 𝐎𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐁𝐥𝐨𝐠 𝐢𝐬 𝐏𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐧𝐨𝐰👉 The Powerful Landscape of Natural Language Processing.
Click: https://bit.ly/2UUeftt
NLP has changed the way we interact with machine and computers. 𝐖𝐡𝐚𝐭 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐚𝐬 𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐝, 𝐡𝐚𝐧𝐝𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐟𝐨𝐫𝐦𝐮𝐥𝐚𝐬 is now a streamlined set of algorithms powered by AI.
𝐍𝐋𝐏 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 will be the underlying force for transformation from data driven to intelligence driven endeavors, as they shape and improve communication technology in the years to come.
The need is a flexible, scalable, secure and governed platform that will provide the decision-makers with a unified platform to track, analyze and manage smart devices.
8 ways pharmaceutical companies ensure success with analytics. Polestar can help you to implement the right use cases in order to set-up the success with analytics. Our experts understand the typical problems faced by pharma companies and have deployed suitable analytics systems that help you derive impact from your data. Feel free to leave a comment below, we will get in touch soon.
Check this out Our latest Blog✔ "Building Smart Factories with IIoT and Analytics."
Manufacturing giants across the world have shifted their focus from a pure-play product focus to building software capabilities. Organizations are investing handsomely into electronics subsystems that are autonomous and are smart in ways that were beyond comprehension just a decade back.
They are employing the help of solutionarchitects,datascientists and user experience professionals to gather all the data in one place to mine, analyse & build a solution around it. The larger is the data set, higher is the possibility to innovate and to deliver a differentiating end-user experience.
Link: https://polestarllp.com/Building-Smart-Factories-with-IoT-and-Analytics-3-Best-Examples-of-IIoT-in-Action.php
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. An effective data management strategy is an
important component for staying competitive. Today,
the huge
volume of structured, semi-structured and
unstructured data is created and real-time analytics on
streaming
data is emerging as an important use case.
The challenge is to come up with a data architecture
that empowers users and enables wide-ranging use
of analytics across the enterprise. Data lakes and Data
warehouse are both core components in modern
data architecture.
3. Data Lakes vs Data Warehouse!
What Are the Differences?
Differences in Technology
A data lake uses a flat architecture
to store a huge amount of raw data
in its native format until it is needed.
There is no fixed limit on account
size or file.
The different data elements in data
lakes are assigned unique identifiers
and tagged with extended metadata
tags.
On the other hand, a hierarchical
data warehouse stores data in files
or folders with a defined schema.
The information in a data
warehouse is stored by subject in
order to assist management make
quick decisions.
4. Differences in Use
Data Lakes are useful for
data scientists because they
allow experimentation on
massive data sets.
The users of data lakes are
usually people who want to
do a thorough analysis of
data.
A data warehouse, measures
and dimensions are conformed
to curable components which
are consistent, governed and
easier for an ever-scalable
audience to consume.
80% of users of data
warehouses are business
users who need refined and
systematic data.
5. Differences in accessibility
and adaptability
A data lake, because it stores all
kinds of data in its raw form, is easily
available for access to any user.
Users are able to explore data in
novel ways.
A data warehouse takes a fairly long
period of time to set up. During its
development, a lot of time is dedicated
to analyzing the sources of data and
how it can be tuned to meet the needs
of a particular business.
Data Lake is a cheaper way to
store/manage data.
Data warehouse is a costlier way to
store/manage data
6. www.polestarllp.com
Final Verdict
The data lake is a game-changer. It not only
saves IT a whole bunch of money, but it also
supports high-end analytics use cases.
Data warehouse, on the other hand, allows
for more strategic use of data.
Organizations typically look at data lakes as
additions to their existing data warehouse.
Data lakes will continue to evolve and play an ever-
increasingly important role in enterprise data
strategy. Enterprises must have an effective data
management architecture in place that includes data
lake.