The document discusses data management on Databricks' lakehouse platform. It describes how Databricks streamlines the data management lifecycle including data ingestion using auto loader and partners, data transformation using Delta Live Tables for ETL, and data analytics using Databricks SQL. The platform unifies data lakes and warehouses to support all phases of data management.
- Data lakes emerged as a concept during the Big Data era and offer a highly flexible way to store both structured and unstructured data using a schema-on-read approach. However, they lack adequate security and authentication mechanisms.
- The document discusses the key concepts of data lakes including how they ingest and store raw data without transforming it initially. It also covers the typical architectural layers of a data lake and some challenges in ensuring proper governance and management of data in the lake.
- Improving data quality, metadata management, and security/access controls are identified as important areas to address some of the current limitations of data lakes.
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.
This document discusses Saxo Bank's plans to implement a data governance solution called the Data Workbench. The Data Workbench will consist of a Data Catalogue and Data Quality Solution to provide transparency into Saxo's data ecosystem and improve data quality. The Data Catalogue will be built using LinkedIn's open source DataHub tool, which provides a metadata search and UI. The Data Quality Solution will use Great Expectations to define and monitor data quality rules. The document discusses why a decentralized, domain-driven approach is needed rather than a centralized solution, and how the Data Workbench aims to establish governance while staying lean and iterative.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Speaking to your data is similar to speak any other language, It starts with understanding the basic terminology and describing key concepts. This presentation will focus on the main/ key steps that are critical to learning the foundation of speaking data.
Organizations have been collecting, storing, and accessing data from the beginning of computerization. Insights gained from analyzing the data enable them to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The well-established data architecture, consisting of a data warehouse, fed from multiple operational data stores, and fronted by BI tools, has served most organizations well. However, over the last two decades, with the explosion of internet-scale data, and the advent of new approaches to data and computational processing, this tried-and-true data architecture has come under strain, and has created both challenges and opportunities for organizations.
In this green paper, we will discuss modern approaches to data architecture that have evolved to address these challenges and provide a framework for companies to build a data architecture and better adapt to increasing demands of the modern business environment. This discussion of data architecture will be tied to the Data Maturity Journey introduced in EQengineered’s June 2021 green paper on Data Modernization.
Unlock Your Data for ML & AI using Data VirtualizationDenodo
How Denodo Complement’s Logical Data Lake in Cloud
● Denodo does not substitute data warehouses, data lakes,
ETLs...
● Denodo enables the use of all together plus other data
sources
○ In a logical data warehouse
○ In a logical data lake
○ They are very similar, the only difference is in the main
objective
● There are also use cases where Denodo can be used as data
source in a ETL flow
- Data lakes emerged as a concept during the Big Data era and offer a highly flexible way to store both structured and unstructured data using a schema-on-read approach. However, they lack adequate security and authentication mechanisms.
- The document discusses the key concepts of data lakes including how they ingest and store raw data without transforming it initially. It also covers the typical architectural layers of a data lake and some challenges in ensuring proper governance and management of data in the lake.
- Improving data quality, metadata management, and security/access controls are identified as important areas to address some of the current limitations of data lakes.
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.
This document discusses Saxo Bank's plans to implement a data governance solution called the Data Workbench. The Data Workbench will consist of a Data Catalogue and Data Quality Solution to provide transparency into Saxo's data ecosystem and improve data quality. The Data Catalogue will be built using LinkedIn's open source DataHub tool, which provides a metadata search and UI. The Data Quality Solution will use Great Expectations to define and monitor data quality rules. The document discusses why a decentralized, domain-driven approach is needed rather than a centralized solution, and how the Data Workbench aims to establish governance while staying lean and iterative.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Speaking to your data is similar to speak any other language, It starts with understanding the basic terminology and describing key concepts. This presentation will focus on the main/ key steps that are critical to learning the foundation of speaking data.
Organizations have been collecting, storing, and accessing data from the beginning of computerization. Insights gained from analyzing the data enable them to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The well-established data architecture, consisting of a data warehouse, fed from multiple operational data stores, and fronted by BI tools, has served most organizations well. However, over the last two decades, with the explosion of internet-scale data, and the advent of new approaches to data and computational processing, this tried-and-true data architecture has come under strain, and has created both challenges and opportunities for organizations.
In this green paper, we will discuss modern approaches to data architecture that have evolved to address these challenges and provide a framework for companies to build a data architecture and better adapt to increasing demands of the modern business environment. This discussion of data architecture will be tied to the Data Maturity Journey introduced in EQengineered’s June 2021 green paper on Data Modernization.
Unlock Your Data for ML & AI using Data VirtualizationDenodo
How Denodo Complement’s Logical Data Lake in Cloud
● Denodo does not substitute data warehouses, data lakes,
ETLs...
● Denodo enables the use of all together plus other data
sources
○ In a logical data warehouse
○ In a logical data lake
○ They are very similar, the only difference is in the main
objective
● There are also use cases where Denodo can be used as data
source in a ETL flow
WP_Impetus_2016_Guide_to_Modernize_Your_Enterprise_Data_Warehouse_JRobertsJane Roberts
The document discusses modernizing enterprise data warehouses to handle big data by migrating workloads to a Hadoop-based data lake. It describes challenges with existing data warehouses and outlines Impetus's automated data warehouse workload migration tool which can help organizations migrate schemas, data, queries and access controls to Hadoop to realize the benefits of big data analytics while protecting existing investments.
Using Data Platforms That Are Fit-For-PurposeDATAVERSITY
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
The document discusses the evolution of data architectures from traditional data warehouses and data lakes to the modern data lakehouse architecture. Specifically, it notes that while data warehouses excel at structured data and queries, and data lakes can store vast amounts of raw data, each have limitations that a new lakehouse architecture aims to address. A lakehouse combines the best of warehouses and lakes by storing all data in a single lake while enabling both SQL/BI and AI/ML workloads directly on that unified data, with consistent security, governance and performance. This overcomes issues of having disjointed and duplicative data silos with different tools and governance between warehouses and lakes.
The Y&L Information Management practice was selected by Rackspace to implement a master data management strategy across multiple applications to address issues with inconsistent data across systems due to rapid growth. Y&L analyzed data sources, created a single data model and mapping, and implemented Informatica data quality jobs to create unique "golden records". A data governance recommendation document was also provided.
Virtualisation de données : Enjeux, Usages & BénéficesDenodo
Watch full webinar here: https://bit.ly/3oah4ng
Gartner a récemment qualifié la Data Virtualisation comme étant une pièce maitresse des architectures d’intégration de données.
Découvrez :
- Les bénéfices d’une plateforme de virtualisation de données
- La multiplication des usages : Lakehouse, Data Science, Big Data, Data Service & IoT
- La création d’une vue unifiée de votre patrimoine de données sans transiger sur la performance
- La construction d’une architecture d’intégration Agile des données : on-premise, dans le cloud ou hybride
Quicker Insights and Sustainable Business Agility Powered By Data Virtualizat...Denodo
Watch full webinar here: https://bit.ly/3xj6fnm
Presented at Chief Data Officer Live 2021 A/NZ
The world is changing faster than ever. And for companies to compete and succeed they need to be agile in order to respond quickly to market changes and emerging opportunities. Data plays an integral role in achieving this business agility. However, given the complex nature of the enterprise data architecture finding and analysing data is an increasingly challenging task. Data virtualization is a modern data integration technique that integrates data in real-time, without having to physically replicate it.
Watch on-demand this session to understand what data virtualization is and how it:
- Delivers data in real-time, and without replication
- Creates a logical architecture to provide a single view of truth
- Centralises the data governance and security framework
- Democratises data for faster decision making and business agility
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
Snowflake and Oracle Autonomous Data Warehouse are two leading cloud data warehouse services. While both aim to simplify data warehousing, Oracle Autonomous Data Warehouse provides more complete automation through its self-driving, self-securing, and self-repairing capabilities. The document finds that Oracle Autonomous Data Warehouse outperforms Snowflake in several key areas including simplicity, automation, performance, security, flexibility and cost. Specifically, Oracle Autonomous Data Warehouse requires less manual intervention, achieves better performance through full automation, and offers significantly lower costs through its superior performance and elasticity controls.
Traditionally, data integration has meant compromise. No matter how rapidly data architects and developers could complete a project before its deadline, speed would always come at the expense of quality. On the other hand, if they focused on delivering a quality project, it would generally drag on for months thus exceeding its deadline. Finally, if the teams concentrated on both quality and rapid delivery, the costs would invariably exceed the budget. Regardless of which path you chose, the end result would be less than desirable. This led some experts to revisit the scope of data integration. This write up shall focus on the same issue.
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/2XXAzU3
So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?
Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.
With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data like through an abstraction layer. Data virtualization can facilitate and expedite accessing and exploring critical data in a cost-effective manner and assist in deriving a greater return on the data lake investment.
You might still not be convinced. Give us an opportunity and join us as we try to bust this myth!
Watch this webinar as we explore the promises of a data lake as well as its downfalls to draw a final conclusion.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Data and Application Modernization in the Age of the Cloudredmondpulver
Data modernization is key to unlocking the full potential of your IT investments, both on premises and in the cloud. Enterprises and organizations of all sizes rely on their data to power advanced analytics, machine learning, and artificial intelligence.
Yet the path to modernizing legacy data systems for the cloud is full of pitfalls that cost time, money, and resources. These issues include high hardware and staffing costs, difficulty moving data and analytical processes to cloud environments, and inadequate support for real-time use cases. These issues delay delivery timelines and increase costs, impacting the return on investment for new, cutting-edge applications.
Watch this webinar in which James Kobielus, TDWI senior research director for data management, explores how enterprises are modernizing their mainframe data and application infrastructures in the cloud to sustain innovation and drive efficiencies. Kobielus will engage John de Saint Phalle, senior product manager at Precisely, in a discussion that addresses the following key questions:
When should enterprises consider migrating and replicating all their data assets to modern public clouds vs. retaining some on-premises in hybrid deployments?How should enterprises modernize their legacy data and application infrastructures to unlock innovation and value in the age of cloud computing?What are the key investments that enterprises should make to modernize their data pipelines to deliver better AI/ML applications in the cloud?What is the optimal data engineering workflow for building, testing, and operationalizing high-quality modern AI/ML applications in the cloud?What value does real-time replication play in migrating data and applications to modern cloud data architectures?What challenges do enterprises face in ensuring and maintaining the integrity, fitness, and quality of the data that they migrate to modern clouds?What tools and methodologies should enterprise application developers use to refactor and transform legacy data applications that have migrated to modern clouds
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
The document discusses Microsoft's approach to implementing a data mesh architecture using their Azure Data Fabric. It describes how the Fabric can provide a unified foundation for data governance, security, and compliance while also enabling business units to independently manage their own domain-specific data products and analytics using automated data services. The Fabric aims to overcome issues with centralized data architectures by empowering lines of business and reducing dependencies on central teams. It also discusses how domains, workspaces, and "shortcuts" can help virtualize and share data across business units and data platforms while maintaining appropriate access controls and governance.
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)Denodo
Watch full webinar here: https://bit.ly/3dudL6u
It's not if you move to the cloud, but when. Most organisations are well underway with migrating applications and data to the cloud. In fact, most organisations - whether they realise it or not - have a multi-cloud strategy. Single, hybrid, or multi-cloud…the potential benefits are huge - flexibility, agility, cost savings, scaling on-demand, etc. However, the challenges can be just as large and daunting. A poorly managed migration to the cloud can leave users frustrated at their inability to get to the data that they need and IT scrambling to cobble together a solution.
In this session, we will look at the challenges facing data management teams as they migrate to cloud and multi-cloud architectures. We will show how the Denodo Platform can:
- Reduce the risk and minimise the disruption of migrating to the cloud.
- Make it easier and quicker for users to find the data that they need - wherever it is located.
- Provide a uniform security layer that spans hybrid and multi-cloud environments.
Watch full webinar here: https://bit.ly/3mdj9i7
You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Watch this on-demand webinar as we cover:
- The most interesting trends in data management
- How to build a data fabric architecture?
- How to manage your data integration strategy in the new hybrid world
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of voice computing in future data analytic
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
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/
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
WP_Impetus_2016_Guide_to_Modernize_Your_Enterprise_Data_Warehouse_JRobertsJane Roberts
The document discusses modernizing enterprise data warehouses to handle big data by migrating workloads to a Hadoop-based data lake. It describes challenges with existing data warehouses and outlines Impetus's automated data warehouse workload migration tool which can help organizations migrate schemas, data, queries and access controls to Hadoop to realize the benefits of big data analytics while protecting existing investments.
Using Data Platforms That Are Fit-For-PurposeDATAVERSITY
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
The document discusses the evolution of data architectures from traditional data warehouses and data lakes to the modern data lakehouse architecture. Specifically, it notes that while data warehouses excel at structured data and queries, and data lakes can store vast amounts of raw data, each have limitations that a new lakehouse architecture aims to address. A lakehouse combines the best of warehouses and lakes by storing all data in a single lake while enabling both SQL/BI and AI/ML workloads directly on that unified data, with consistent security, governance and performance. This overcomes issues of having disjointed and duplicative data silos with different tools and governance between warehouses and lakes.
The Y&L Information Management practice was selected by Rackspace to implement a master data management strategy across multiple applications to address issues with inconsistent data across systems due to rapid growth. Y&L analyzed data sources, created a single data model and mapping, and implemented Informatica data quality jobs to create unique "golden records". A data governance recommendation document was also provided.
Virtualisation de données : Enjeux, Usages & BénéficesDenodo
Watch full webinar here: https://bit.ly/3oah4ng
Gartner a récemment qualifié la Data Virtualisation comme étant une pièce maitresse des architectures d’intégration de données.
Découvrez :
- Les bénéfices d’une plateforme de virtualisation de données
- La multiplication des usages : Lakehouse, Data Science, Big Data, Data Service & IoT
- La création d’une vue unifiée de votre patrimoine de données sans transiger sur la performance
- La construction d’une architecture d’intégration Agile des données : on-premise, dans le cloud ou hybride
Quicker Insights and Sustainable Business Agility Powered By Data Virtualizat...Denodo
Watch full webinar here: https://bit.ly/3xj6fnm
Presented at Chief Data Officer Live 2021 A/NZ
The world is changing faster than ever. And for companies to compete and succeed they need to be agile in order to respond quickly to market changes and emerging opportunities. Data plays an integral role in achieving this business agility. However, given the complex nature of the enterprise data architecture finding and analysing data is an increasingly challenging task. Data virtualization is a modern data integration technique that integrates data in real-time, without having to physically replicate it.
Watch on-demand this session to understand what data virtualization is and how it:
- Delivers data in real-time, and without replication
- Creates a logical architecture to provide a single view of truth
- Centralises the data governance and security framework
- Democratises data for faster decision making and business agility
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
Snowflake and Oracle Autonomous Data Warehouse are two leading cloud data warehouse services. While both aim to simplify data warehousing, Oracle Autonomous Data Warehouse provides more complete automation through its self-driving, self-securing, and self-repairing capabilities. The document finds that Oracle Autonomous Data Warehouse outperforms Snowflake in several key areas including simplicity, automation, performance, security, flexibility and cost. Specifically, Oracle Autonomous Data Warehouse requires less manual intervention, achieves better performance through full automation, and offers significantly lower costs through its superior performance and elasticity controls.
Traditionally, data integration has meant compromise. No matter how rapidly data architects and developers could complete a project before its deadline, speed would always come at the expense of quality. On the other hand, if they focused on delivering a quality project, it would generally drag on for months thus exceeding its deadline. Finally, if the teams concentrated on both quality and rapid delivery, the costs would invariably exceed the budget. Regardless of which path you chose, the end result would be less than desirable. This led some experts to revisit the scope of data integration. This write up shall focus on the same issue.
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/2XXAzU3
So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?
Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.
With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data like through an abstraction layer. Data virtualization can facilitate and expedite accessing and exploring critical data in a cost-effective manner and assist in deriving a greater return on the data lake investment.
You might still not be convinced. Give us an opportunity and join us as we try to bust this myth!
Watch this webinar as we explore the promises of a data lake as well as its downfalls to draw a final conclusion.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Data and Application Modernization in the Age of the Cloudredmondpulver
Data modernization is key to unlocking the full potential of your IT investments, both on premises and in the cloud. Enterprises and organizations of all sizes rely on their data to power advanced analytics, machine learning, and artificial intelligence.
Yet the path to modernizing legacy data systems for the cloud is full of pitfalls that cost time, money, and resources. These issues include high hardware and staffing costs, difficulty moving data and analytical processes to cloud environments, and inadequate support for real-time use cases. These issues delay delivery timelines and increase costs, impacting the return on investment for new, cutting-edge applications.
Watch this webinar in which James Kobielus, TDWI senior research director for data management, explores how enterprises are modernizing their mainframe data and application infrastructures in the cloud to sustain innovation and drive efficiencies. Kobielus will engage John de Saint Phalle, senior product manager at Precisely, in a discussion that addresses the following key questions:
When should enterprises consider migrating and replicating all their data assets to modern public clouds vs. retaining some on-premises in hybrid deployments?How should enterprises modernize their legacy data and application infrastructures to unlock innovation and value in the age of cloud computing?What are the key investments that enterprises should make to modernize their data pipelines to deliver better AI/ML applications in the cloud?What is the optimal data engineering workflow for building, testing, and operationalizing high-quality modern AI/ML applications in the cloud?What value does real-time replication play in migrating data and applications to modern cloud data architectures?What challenges do enterprises face in ensuring and maintaining the integrity, fitness, and quality of the data that they migrate to modern clouds?What tools and methodologies should enterprise application developers use to refactor and transform legacy data applications that have migrated to modern clouds
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
The document discusses Microsoft's approach to implementing a data mesh architecture using their Azure Data Fabric. It describes how the Fabric can provide a unified foundation for data governance, security, and compliance while also enabling business units to independently manage their own domain-specific data products and analytics using automated data services. The Fabric aims to overcome issues with centralized data architectures by empowering lines of business and reducing dependencies on central teams. It also discusses how domains, workspaces, and "shortcuts" can help virtualize and share data across business units and data platforms while maintaining appropriate access controls and governance.
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)Denodo
Watch full webinar here: https://bit.ly/3dudL6u
It's not if you move to the cloud, but when. Most organisations are well underway with migrating applications and data to the cloud. In fact, most organisations - whether they realise it or not - have a multi-cloud strategy. Single, hybrid, or multi-cloud…the potential benefits are huge - flexibility, agility, cost savings, scaling on-demand, etc. However, the challenges can be just as large and daunting. A poorly managed migration to the cloud can leave users frustrated at their inability to get to the data that they need and IT scrambling to cobble together a solution.
In this session, we will look at the challenges facing data management teams as they migrate to cloud and multi-cloud architectures. We will show how the Denodo Platform can:
- Reduce the risk and minimise the disruption of migrating to the cloud.
- Make it easier and quicker for users to find the data that they need - wherever it is located.
- Provide a uniform security layer that spans hybrid and multi-cloud environments.
Watch full webinar here: https://bit.ly/3mdj9i7
You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Watch this on-demand webinar as we cover:
- The most interesting trends in data management
- How to build a data fabric architecture?
- How to manage your data integration strategy in the new hybrid world
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of voice computing in future data analytic
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
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/
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
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Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
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Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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2. Given the changing work environment, with more remote workers and new channels, we are
seeing greater importance placed on data management.
According to Gartner, “The shift from centralized to distributed working
requires organizations to make data, and data management capabilities,
available more rapidly and in more places than ever before.”
Data management has been a common practice across industries for many years, although
not all organizations have used the term the same way. At Databricks, we view data
management as all disciplines related to managing data as a strategic and valuable resource,
which includes collecting data, processing data, governing data, sharing data, analyzing it —
and doing this all in a cost-efficient, effective and reliable manner.
Introduction
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3. Introduction 2
The challenges of data management 4
Data management on Databricks 6
Data ingestion 7
Data transformation, quality and processing 10
Data analytics 13
Data governance 15
Data sharing 17
Conclusion 19
Contents
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4. Ultimately, the consistent and reliable flow of data across people, teams and business
functions is crucial to an organization’s survival and ability to innovate. And while we are
seeing companies realize the value of their data — through data-driven product decisions,
more collaboration or rapid movement into new channels — most businesses struggle to
manage and leverage data correctly.
According to Forrester, up to 73% of company data goes
unused for analytics and decision-making, a metric that is
costing businesses their success.
The vast majority of company data today flows into a data lake, where teams do data prep
and validation in order to serve downstream data science and machine learning initiatives.
At the same time, a huge amount of data is transformed and sent to many different
downstream data warehouses for business intelligence (BI), because traditional data lakes
are too slow and unreliable for BI workloads.
Depending on the workload, data sometimes also needs to be moved out of the data
warehouse back to the data lake. And increasingly, machine learning workloads are also
reading and writing to data warehouses. The underlying reason why this kind of data
management is challenging is that there are inherent differences between data lakes and
data warehouses.
The challenges of
data management
Data
Sharing
Data
Management
Data
Governance
Data
Analytics
Data
Transformation
and Processing
Data
Ingestion
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5. On one hand, data lakes do a great job supporting machine learning — they have open
formats and a big ecosystem — but they have poor support for business intelligence and
suffer from complex data quality problems. On the other hand, we have data warehouses
that are great for BI applications, but they have limited support for machine learning
workloads, and they are proprietary systems with only a SQL interface.
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6. Unifying these systems can be transformational in how we think about data. And the
Databricks Lakehouse Platform does just that — unifies all these disparate workloads, teams
and data, and provides an end-to-end data management solution for all phases of the data
management lifecycle. And with Delta Lake bringing reliability, performance and security to
a data lake — and forming the foundation of a lakehouse — data engineers can avoid these
architecture challenges. Let’s take a look at the phases of data management on Databricks.
Data management
on Databricks
Learn more about the
Databricks Lakehouse Platform
Learn more about Delta Lake
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7. In today’s world, IT organizations are inundated with data siloed across various on-premises
application systems, databases, data warehouses and SaaS applications. This fragmentation
makes it difficult to support new use cases for analytics or machine learning. To support
these new use cases and the growing volume and complexity of data, many IT teams are
now looking to centralize all their data with a lakehouse architecture built on top of Delta
Lake, an open format storage layer.
However, the biggest challenge data engineers face in supporting the lakehouse architecture
is efficiently moving data from various systems into their lakehouse. Databricks offers two
ways to easily ingest data into the lakehouse: through a network of data ingestion partners or
by easily ingesting data into Delta Lake with Auto Loader.
Data ingestion
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8. The network of data ingestion partners makes it possible to move data from various siloed
systems into the lake. The partners have built native integrations with Databricks to ingest
and store data in Delta Lake, making data easily accessible for data teams to work with.
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9. On the other hand, many IT organizations have been using cloud storage, such as AWS
S3, Microsoft Azure Data Lake Storage or Google Cloud Storage, and have implemented
methods to ingest data from various systems. Databricks Auto Loader optimizes file sources,
infers schema and incrementally processes new data as it lands in a cloud store with exactly
once guarantees, low cost, low latency and minimal DevOps work.
With Auto Loader, data engineers provide a source directory path and start the ingestion
job. The new structured streaming source, called “cloudFiles,” will automatically set up file
notification services that subscribe file events from the input directory and process new
files as they arrive, with the option of also processing existing files in that directory.
Getting all the data into the lakehouse is critical to unify machine learning and analytics.
With Databricks Auto Loader and our extensive partner integration capabilities, data
engineering teams can efficiently move any data type to the data lake.
Learn more
Data ingestion on Databricks
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10. Moving data into the lakehouse solves one of the data management challenges, but in order
to make data usable by data analysts or data scientists, data must also be transformed into
a clean, reliable source. This is an important step, as outdated or unreliable data can lead to
mistakes, inaccuracies or distrust of the insights derived.
Data engineers have the difficult and laborious task of cleansing complex, diverse data and
transforming it into a format fit for analysis, reporting or machine learning. This requires the
data engineer to know the ins and outs of the data infrastructure platform, and requires the
building of complex queries (transformations) in various languages, stitching together queries
for production. For many organizations, this complexity in the data management phase limits
their ability for downstream analysis, data science and machine learning.
Data transformation,
quality and processing
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11. To help eliminate the complexity, Databricks Delta Live Tables (DLT) gives data engineering
teams a massively scalable ETL framework to build declarative data pipelines in SQL or
Python. With DLT, data engineers can apply in-line data quality parameters to manage
governance and compliance with deep visibility into data pipeline operations on a fully
managed and secure lakehouse platform across multiple clouds.
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12. DLT provides a simple way of creating, standardizing and maintaining ETL. DLT data pipelines
automatically adapt to changes in the data, code or environment, allowing data engineers to
focus on developing, validating and testing data that is being transformed. To deliver trusted
data, data engineers define rules about the expected quality of data within the data pipeline.
DLT enables teams to analyze and monitor data quality continuously to reduce the spread of
incorrect and inconsistent data.
“Delta Live Tables has helped our teams save time and effort in managing
data at scale...With this capability augmenting the existing lakehouse
architecture, Databricks is disrupting the ETL and data warehouse markets,
which is important for companies like ours.”
— Dan Jeavons, General Manager, Data Science, Shell
A key aspect of successful data engineering implementation is having engineers focus on
developing and testing ETL and spending less time on building out infrastructure. Delta Live
Tables abstracts the underlying data pipeline definition from the pipeline execution. This
means at pipeline execution, DLT optimizes the pipeline, automatically builds the execution
graph for the underlying data pipeline queries, manages the infrastructure with dynamic
resourcing and provides a visual graph for end-to-end pipeline visibility on overall pipeline
health for performance, latency, quality and more.
With all these DLT components in place, data engineers can focus solely on transforming,
cleansing and delivering quality data for machine learning and analytics.
Learn more
Data transformation on Databricks
with Delta Live Tables
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13. Now that data is available for consumption, data analysts can derive insights to drive business
decisions. Typically, to access well-conformed data within a data lake, an analyst would need
to leverage Apache Spark™ or use a developer interface to access data. To simplify access
and query a lakehouse, Databricks SQL allows data analysts to perform deeper analysis with
a SQL-native experience to run BI and SQL workloads on a multicloud lakehouse architecture.
Databricks SQL complements existing BI tools with a SQL-native interface that allows data
analysts and data scientists to query data lake data directly within Databricks.
A dedicated SQL workspace brings
familiarity for data analysts to run ad
hoc queries on the lakehouse, create rich
visualizations to explore queries from
a different perspective and organize
those visualizations into drag-and-drop
dashboards, which can be shared with
stakeholders across the organization.
Within the workspace, analysts can
explore schema, save queries as
snippets for reuse and schedule queries
for automatic refresh.
Data analytics
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14. Customers can maximize existing investments by connecting their preferred BI tools to their
lakehouse with Databricks SQL Endpoints. Re-engineered and optimized connectors ensure
fast performance, low latency and high user concurrency to your data lake. This means that
analysts can use the best tool for the job on one single source of truth for your data while
minimizing more ETL and data silos.
“Now more than ever, organizations need a data strategy that enables speed
and agility to be adaptable. As organizations are rapidly moving their data
to the cloud, we’re seeing growing interest in doing analytics on the data
lake. The introduction of Databricks SQL delivers an entirely new experience
for customers to tap into insights from massive volumes of data with the
performance, reliability and scale they need. We’re proud to partner with
Databricks to bring that opportunity to life.”
— Francois Ajenstat, Chief Product Officer, Tableau
Finally, for governance and administration, administrators can apply SQL data access
controls on tables for fine-grain control and visibility over how data is used and accessed
across the entire lakehouse for analytics. Administrators have visibility into Databricks SQL
usage: the history of all executed queries to understand performance, where each query ran,
how long a query ran and which user ran the workload. All this information is captured and
made available for administrators to easily triage, troubleshoot and understand performance.
Learn more
Data analytics on Databricks
with Databricks SQL
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15. Many organizations start building out data lakes as a means to solve for analytics and
machine learning, making data governance an afterthought. But with the rapid adoption
of lakehouse architectures, data is being democratized and accessed throughout the
organization. To govern data lakes, administrators have relied on cloud-vendor-specific
security controls, such as IAM roles or RBAC and file-oriented access control to manage
data. However, this technical security mechanism does not meet the requirements for data
governance and of data teams. Data governance defines who within an organization has
authority and control over data assets and how those assets may be used.
To more effectively govern data, the Databricks Unity Catalog brings fine-grain governance
and security to the lakehouse using standard ANSI SQL or a simple UI, enabling data
stewards to safely open their lakehouse for broad internal consumption. With the SQL-based
interface, data stewards will be able to apply attribute-based access controls to tag and
apply policies to similar data objects with the same attribute. Additionally, data stewards can
apply strong governance to other data assets like ML models, dashboards and external data
sources all within the same interface.
As organizations modernize their data platforms from on-premises to cloud, many are
moving beyond a single-cloud environment for governing data. Instead, they’re choosing a
multicloud strategy, often working with the three leading cloud providers — AWS, Azure and
GCP — across geographic regions. Managing all this data across multiple cloud platforms,
storage and other catalogs can be a challenge for democratizing data throughout an
organization. The Unity Catalog will enable a secure single point of control to centrally
manage, track and audit data trails.
Data governance
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16. Finally, Unity Catalog will make it easy to discover, describe, audit and govern data assets
from one central location. Data stewards can set or review all permissions visually, and the
catalog captures audit and lineage information that shows you how each data asset was
produced and accessed. Data lineage, role-based security policies, table or column level
tags, and central auditing capabilities will make it easy for data stewards to confidently
manage and secure data access to meet compliance and privacy needs, directly on the
lakehouse. The UI is designed for collaboration so that data users will be able to document
each asset and see who uses it.
Data governance on Databricks
with Unity Catalog
Learn more
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17. As organizations stand up lakehouse architectures, the supply and demand of cleansed and
trusted data doesn’t end with analytics and machine learning. As many IT leaders realize in
today’s data-driven economy, sharing data across organizations — with customers, partners
and suppliers — is a key determinant of success in gaining more meaningful insights.
However, many organizations fail at data sharing due to a lack of standards, collaboration
difficulties when working with large data sets across a large ecosystem of systems or tools,
and mitigating risk while sharing data. To address these challenges, Delta Sharing, an open
protocol for secure real-time data sharing, simplifies cross-organizational data sharing.
Data sharing
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18. Integrated with the Databricks Lakehouse Platform, Delta Sharing will allow providers to easily
use their existing data or workflows to securely share live data in Delta Lake or Apache Parquet
format — without copying it to any other servers or cloud object stores. With Delta Sharing’s
open protocol, data consumers will be able to easily access shared data directly by using open
source clients (such as pandas) or commercial BI, analytics or governance clients — data
consumers don’t need to be on the same platform as providers. The protocol is designed with
privacy and compliance requirements in mind. Delta Sharing will give administrators security
and privacy controls for granting access to and for tracking and auditing shared data from a
single point of enforcement.
Delta Sharing is the industry’s first open protocol for secure data sharing, making it simple to
share data with other organizations regardless of which computing platforms they use. Delta
Sharing will be able to seamlessly share existing large-scale data sets based on the Apache
Parquet and Delta Lake formats, and will be supported in the Delta Lake open source project
so that existing engines that support Delta Lake can easily implement it.
Learn more
Sharing data on Databricks
with Delta Sharing
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19. As we move forward and transition to new ways of working, adopt new technologies
and scale operations, investing in effective data management is critical to removing the
bottleneck in modernization. With the Databricks Lakehouse Platform, you can manage your
data from ingestion to analytics and truly unify data, analytics and AI.
Conclusion
Learn more about data management on
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