API security is critical to digital businesses as the economy doubles down on operational continuity, speed, and agility. Every day, new threats and vulnerabilities are created, and every day, companies find themselves racing against the clock to patch them.
Watch our on-demand webinar with MuleSoft to learn how to construct multi-layer defences against attacks and safeguard the edge of your network, every API, and your data:
https://www.whishworks.com/event/webinar-implementing-your-apis-with-zero-trust/
Key topics:
– Eliminate vulnerabilities at the network edge based on observed attack patterns at the API gateway
– Enforce security by configuring mandatory policies
– Hide sensitive data with format-preserving tokenization to reduce compliance scope
Organisations using Salesforce will inevitably accumulate technical debt over time. It’s a costly side effect of growth, and to manage it successfully, these organisations need to not only remove their existing debt but also understand its causes and develop a plan to manage it in the future.
To find out more about the key areas you need to cover to carry out a successful technical debt assessment in the Salesforce platform watch our on-demand webinar:
https://www.whishworks.com/event/recording-performing-a-successful-technical-debt-assessment-in-salesforce/
Key topics
– What is technical debt
– Causes of technical debt in Salesforce
– Key areas to assess
– Common tools for diagnosis
– Technical debt assessment results & reporting
In a recent webinar with Confluent, we discussed the many benefits of event streaming for banks, and showcased how forward looking financial institutions are getting ahead of the curve with real-time streaming. These are the key highlights.
To find out more, watch the webinar on-demand here:
https://www.whishworks.com/event/webinar-transforming-banking-with-event-streaming/
Mule runtime is the engine for Anypoint Platform, combining data and application integration across legacy systems, SaaS applications, and APIs. Mule 4 is the latest and most advanced version of Mule runtime engine and has been designed to boost scalability and support performance optimisation and smooth upgrade.
There can be many reasons for you to start considering upgrading to the newest version of Mule. These four are the ones we encounter the most:
1. The Mule version you’re using reaches End-of-Support or End-of-Life
2. You want to make significant updates to the existing applications
3. You want to take advantage of key Mule 4 capabilities
4. You decide to upgrade all of your apps to Mule 4 so that they are on one version
In this guide we cover the areas that we consider the most important for correctly planning and executing your migration to avoid unwelcome delays and surprises that will increase cost and effort.
Predictive Analytics enables organisations to forecast future events, analyse risks and opportunities, and automate decision making processes by analysing historic data.
In this presentation we take a classic example of when we have both traditional databases like Salesforce, SAP, and MySQL, and big data databases that deal with a huge amount of data that would not be possible to do using the traditional databases. Leveraging Anypoint Platform and the relevant connectors, you can start innovating without the complexity that is usually associated with Big Data.
During the recent London MuleSoft Summit, we asked CIOs, IT leaders, integration architects and development leads to tell us about their progress with integrating their systems and applications. This is what they told us.
API security is critical to digital businesses as the economy doubles down on operational continuity, speed, and agility. Every day, new threats and vulnerabilities are created, and every day, companies find themselves racing against the clock to patch them.
Watch our on-demand webinar with MuleSoft to learn how to construct multi-layer defences against attacks and safeguard the edge of your network, every API, and your data:
https://www.whishworks.com/event/webinar-implementing-your-apis-with-zero-trust/
Key topics:
– Eliminate vulnerabilities at the network edge based on observed attack patterns at the API gateway
– Enforce security by configuring mandatory policies
– Hide sensitive data with format-preserving tokenization to reduce compliance scope
Organisations using Salesforce will inevitably accumulate technical debt over time. It’s a costly side effect of growth, and to manage it successfully, these organisations need to not only remove their existing debt but also understand its causes and develop a plan to manage it in the future.
To find out more about the key areas you need to cover to carry out a successful technical debt assessment in the Salesforce platform watch our on-demand webinar:
https://www.whishworks.com/event/recording-performing-a-successful-technical-debt-assessment-in-salesforce/
Key topics
– What is technical debt
– Causes of technical debt in Salesforce
– Key areas to assess
– Common tools for diagnosis
– Technical debt assessment results & reporting
In a recent webinar with Confluent, we discussed the many benefits of event streaming for banks, and showcased how forward looking financial institutions are getting ahead of the curve with real-time streaming. These are the key highlights.
To find out more, watch the webinar on-demand here:
https://www.whishworks.com/event/webinar-transforming-banking-with-event-streaming/
Mule runtime is the engine for Anypoint Platform, combining data and application integration across legacy systems, SaaS applications, and APIs. Mule 4 is the latest and most advanced version of Mule runtime engine and has been designed to boost scalability and support performance optimisation and smooth upgrade.
There can be many reasons for you to start considering upgrading to the newest version of Mule. These four are the ones we encounter the most:
1. The Mule version you’re using reaches End-of-Support or End-of-Life
2. You want to make significant updates to the existing applications
3. You want to take advantage of key Mule 4 capabilities
4. You decide to upgrade all of your apps to Mule 4 so that they are on one version
In this guide we cover the areas that we consider the most important for correctly planning and executing your migration to avoid unwelcome delays and surprises that will increase cost and effort.
Predictive Analytics enables organisations to forecast future events, analyse risks and opportunities, and automate decision making processes by analysing historic data.
In this presentation we take a classic example of when we have both traditional databases like Salesforce, SAP, and MySQL, and big data databases that deal with a huge amount of data that would not be possible to do using the traditional databases. Leveraging Anypoint Platform and the relevant connectors, you can start innovating without the complexity that is usually associated with Big Data.
During the recent London MuleSoft Summit, we asked CIOs, IT leaders, integration architects and development leads to tell us about their progress with integrating their systems and applications. This is what they told us.
With the recent release of Mule 4, WHISHWORKS, MuleSoft and Flyin.com got together to discuss what’s new in Anypoint Platform’s new engine and how to streamline the migration from Mule 3 to Mule 4.
The agenda included:
Customer Success Story
What’s new in Anypoint Platform
What’s new in Mule 4
Upcoming Mule 4 Migration toolkits:
- MuleSoft Application Migration Toolkit
- WHISHWORKS Custom Migration Toolkit
Migrating Mule 3 Connectors to Mule 4 with Mule SDK
- Demo
Adapting Mule 4
We asked delegates at the recent Big Data Analytics and MapR Convergence events in London, about their progress with implementing Big Data in their organisations. Here is what they told us.
SUEZ is a large, global waste and water management company with over 84,000 employees. It has been transforming its business and IT systems to make data and information the center of its operations. SUEZ implemented an API-led connectivity approach using MuleSoft to help overcome limitations of its legacy systems and enable faster, more flexible application development and integration of key systems like SAP. This new approach supports SUEZ's goals of becoming a more data-driven, customer-centric organization.
The document discusses telecom service integration from the perspectives of mobile users, enterprises, and telecom operators. It notes that mobile users want to connect their devices and access services through a single platform. Enterprises want tools to minimize time to market while maximizing profits. This has led companies to focus on technology that enables quick integration across platforms. The telecom industry relies on a multi-vendor environment and integration is challenging due to different products and systems. Enterprise service buses like Mulesoft ESB are playing a key role in meeting these integration needs. Speed, legislative compliance, and smart pricing are also discussed as important factors.
1) The document discusses microservices architecture as an alternative to monolithic architecture for building applications. Microservices split applications into independently deployable services organized around business capabilities rather than being a single application.
2) It compares monolithic and microservices architecture, noting that microservices allow individual services to be deployed and scaled independently rather than requiring the entire application to be redeployed.
3) The document argues that MuleSoft's integration platform can be used to implement microservices architecture by creating each service or API as a separate application that can then be independently deployed and managed.
MOM provides a clean method of communication between disparate software applications and emerged as a approach that distributed enterprise systems are built
This presentation focuses to bring the goodness of JSR303 Bean Validation model [Hibernate RI], Spring framework’s support for custom validation and a blend on how it will work in MuleSoft ESB.
The document discusses how to deploy Mule ESB applications using the Mule Management Console (MMC). MMC provides a centralized interface to monitor, manage, and administer Mule ESB instances. To deploy an application, users log into MMC and use the Deployments tab to provision applications by deploying, undeploying, or redeploying them to target Mule ESB servers. The Deployments tab displays all provisioned applications and their statuses. Users can create new deployment groups by specifying applications, servers, and server groups for the group.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
With the recent release of Mule 4, WHISHWORKS, MuleSoft and Flyin.com got together to discuss what’s new in Anypoint Platform’s new engine and how to streamline the migration from Mule 3 to Mule 4.
The agenda included:
Customer Success Story
What’s new in Anypoint Platform
What’s new in Mule 4
Upcoming Mule 4 Migration toolkits:
- MuleSoft Application Migration Toolkit
- WHISHWORKS Custom Migration Toolkit
Migrating Mule 3 Connectors to Mule 4 with Mule SDK
- Demo
Adapting Mule 4
We asked delegates at the recent Big Data Analytics and MapR Convergence events in London, about their progress with implementing Big Data in their organisations. Here is what they told us.
SUEZ is a large, global waste and water management company with over 84,000 employees. It has been transforming its business and IT systems to make data and information the center of its operations. SUEZ implemented an API-led connectivity approach using MuleSoft to help overcome limitations of its legacy systems and enable faster, more flexible application development and integration of key systems like SAP. This new approach supports SUEZ's goals of becoming a more data-driven, customer-centric organization.
The document discusses telecom service integration from the perspectives of mobile users, enterprises, and telecom operators. It notes that mobile users want to connect their devices and access services through a single platform. Enterprises want tools to minimize time to market while maximizing profits. This has led companies to focus on technology that enables quick integration across platforms. The telecom industry relies on a multi-vendor environment and integration is challenging due to different products and systems. Enterprise service buses like Mulesoft ESB are playing a key role in meeting these integration needs. Speed, legislative compliance, and smart pricing are also discussed as important factors.
1) The document discusses microservices architecture as an alternative to monolithic architecture for building applications. Microservices split applications into independently deployable services organized around business capabilities rather than being a single application.
2) It compares monolithic and microservices architecture, noting that microservices allow individual services to be deployed and scaled independently rather than requiring the entire application to be redeployed.
3) The document argues that MuleSoft's integration platform can be used to implement microservices architecture by creating each service or API as a separate application that can then be independently deployed and managed.
MOM provides a clean method of communication between disparate software applications and emerged as a approach that distributed enterprise systems are built
This presentation focuses to bring the goodness of JSR303 Bean Validation model [Hibernate RI], Spring framework’s support for custom validation and a blend on how it will work in MuleSoft ESB.
The document discusses how to deploy Mule ESB applications using the Mule Management Console (MMC). MMC provides a centralized interface to monitor, manage, and administer Mule ESB instances. To deploy an application, users log into MMC and use the Deployments tab to provision applications by deploying, undeploying, or redeploying them to target Mule ESB servers. The Deployments tab displays all provisioned applications and their statuses. Users can create new deployment groups by specifying applications, servers, and server groups for the group.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.