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  • 3 min read

Guide to AWS Database Migration Service (DMS)

Hello, everyone! Welcome back to another episode of Continuous Improvement. I'm your host, Victor Leung, and today we're diving into a very pertinent topic in the world of cloud computing — the AWS Database Migration Service, commonly known as DMS. Whether you're a database administrator, a solution architect, or someone interested in the intricacies of migrating databases to the cloud, this episode is for you.

As a Solution Architect, I've worked with numerous clients who have considered or utilized AWS DMS for their database migration needs. It's a powerful tool with a lot to offer, but like any technology, it comes with its own set of strengths and weaknesses. Let’s break down what AWS DMS is all about, starting with the pros.

First off, AWS DMS supports a wide range of databases, from Oracle and MySQL to PostgreSQL and beyond. This versatility makes it a go-to solution for many businesses. Another significant advantage is the minimal downtime it offers during migrations. We all know that in today’s fast-paced world, downtime can be quite costly. DMS also scores high on ease of use with its user-friendly interface, making it accessible to those who might not be deeply technical.

On top of that, for businesses dealing with large databases, DMS can scale to your needs, ensuring that even the most substantial data loads can be handled efficiently. And let’s not forget about its continuous data replication capabilities, which are crucial for keeping your new database synchronized until you completely cut over from the old system.

But it’s not all smooth sailing. One of the primary drawbacks of AWS DMS is its limited capabilities in transforming data during the migration process. If your migration requires significant data transformation, DMS might not be enough. Additionally, while designed to minimize performance overhead, the migration process can still introduce some, especially with large or complex databases.

Another point to consider is the dependency on network bandwidth. A lack of sufficient bandwidth can slow down the migration process significantly. And although DMS is user-friendly, there’s still a learning curve involved, particularly when it comes to configuring and optimizing the service for specific needs.

Now, when should you consider using AWS DMS? It’s ideal for homogeneous migrations, like upgrading from one version of a database to another, or even for heterogeneous migrations, where you're moving from one database platform to another entirely. It’s also useful for setting up disaster recovery systems or maintaining continuous data replication for analytics.

However, it’s important to recognize when DMS might not be the best fit. For example, if your migration involves complex transformations, or if you're dealing with very large databases that have high transaction rates, you might encounter challenges that DMS isn't equipped to handle efficiently. Also, if you’re using a database engine that isn’t supported by DMS, you’ll need to look at alternative methods.

In conclusion, AWS DMS is a formidable tool in the right scenarios, offering ease of use, scalability, and minimal downtime. However, understanding both its strengths and limitations is crucial in determining whether it’s the right solution for your specific needs. Like any good architect or developer, weighing these pros and cons will ensure you make the best decision for your organization.

That wraps up our discussion on AWS Database Migration Service. Thanks for tuning in to Continuous Improvement. If you have any questions or want to share your experiences with AWS DMS, feel free to reach out on social media or comment below. Don’t forget to subscribe for more insights on how you can keep evolving in the digital landscape. Until next time, keep improving and keep innovating.

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  • 2 min read

Understanding AWS Aurora Replica vs Cloning

Hello, everyone, and welcome back to Continuous Improvement. I’m your host, Victor Leung, diving deep into the world of cloud databases with a focus on Amazon Aurora today. Whether you're managing massive datasets or looking for scalable solutions, understanding Aurora’s capabilities, especially regarding its replicas and cloning features, is crucial. Let’s break it down and help you choose the best options for your scenarios.

Let’s start with Aurora Replicas. These are read-only copies of your primary database. What’s fascinating here is that these replicas share the same underlying storage as the primary, meaning that data replication is nearly instantaneous. This setup is ideal for scaling out read operations without a hitch and boosting the availability of your database across the board.

Aurora offers two types of replicas. First, the standard Aurora Replicas, which are great for reducing read latency and can scale up to 15 replicas per primary instance. Then, there are Cross-Region Replicas, perfect for those looking to expand globally or implement robust disaster recovery plans by placing replicas in different geographic locations.

Think of scenarios where you have high read workloads. Aurora Replicas let you distribute this traffic across multiple copies to maintain performance. Plus, in the event of a primary instance failure, you can promote a replica to keep your services running smoothly — crucial for maintaining high availability. And for businesses going global, positioning replicas closer to your end-users can drastically improve application responsiveness.

Now, shifting gears, let’s talk about Aurora Cloning. Unlike replicas, cloning is about creating a quick copy of your database using a copy-on-write mechanism. This means the clone starts off sharing data with the source and only diverges when changes occur. It’s a brilliant feature for when you need rapid clones without racking up extra storage costs.

Cloning shines in development and testing. Imagine you’re about to roll out a new feature. With cloning, you can spin up a test environment in no time, ensuring your new additions don’t impact your live database. It’s also invaluable for snapshot analysis or managing short-term, intense workloads without disturbing your primary database’s performance.

So, how do you choose? If your goal is to enhance read performance or ensure seamless failover capabilities, Aurora Replicas are your go-to. But if you need to set up isolated testing environments or handle temporary workload spikes, cloning is the way forward.

Each feature has its place in managing modern cloud databases, and your choice will depend on your specific needs regarding scalability, cost, and operational flexibility.

That wraps up our exploration of Amazon Aurora’s replicas and cloning capabilities. Thanks for tuning in to Continuous Improvement. If you have any questions or if there’s a topic you’d like us to cover, drop a comment or connect with me on LinkedIn. Remember, the right knowledge can propel you forward, so keep learning and keep improving. Until next time, take care and stay innovative!

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  • 2 min read

How I Study and Prepare for AWS Certification Exams

Welcome to Continuous Improvement, the podcast where we explore the technologies and strategies that drive our digital world. I’m Victor Leung, and today we’re diving into the world of AWS certifications. Whether you’re just starting out or aiming to expand your AWS knowledge, I’ve got some tips and insights from my own journey that might just help you on your path to certification.

Let’s kick things off with a bit of background. I started my AWS certification journey back in July 2020, and since then, I've managed to earn several certifications across different specialties. From Solutions Architect to DevOps Engineer, each certification has been a stepping stone in deepening my understanding of the AWS ecosystem.

One thing I’ve learned along the way is the importance of identifying your knowledge gaps early. Starting with practice questions is a great way to pinpoint areas where you need more focus. Once you know what you need to work on, you can tailor your study efforts more effectively.

And speaking of study efforts, let me share some resources that have been invaluable in my preparation. Platforms like A Cloud Guru offer comprehensive courses and labs tailored to various AWS certifications. For those who prefer a more direct teaching style, Stephane Maarek’s courses on Udemy are fantastic for breaking down complex concepts.

Books are another excellent resource. For instance, the "AWS Certified Security Specialty All-in-One Exam Guide" by Tracy Pierce is a must-read if you’re gearing up for the Security Specialty exam. These materials provide detailed insights and real-world scenarios that can really help cement your understanding of AWS services.

Now, it’s not just about hitting the books or completing courses. Hands-on practice is crucial. Using the AWS Free Tier to experiment with services can help you understand their practical applications and how they interconnect.

Another tip? Don’t go it alone. Joining study groups and engaging with the community can provide both support and new perspectives. Plus, it's a great way to keep your learning on track and stay motivated.

And let’s not forget about the mental aspect of studying. Taking regular breaks can help prevent burnout and improve information retention. Remember, this is a marathon, not a sprint.

In conclusion, whether you’re looking to enhance your career, boost your skills, or just satisfy your curiosity about cloud technologies, AWS certifications are a great pathway. But remember, the journey is your own. Find the resources and methods that work best for you, and take it one step at a time.

Thanks for tuning into Continuous Improvement. If you enjoyed today’s episode, don’t forget to subscribe and share it with someone who might benefit from our discussion. Until next time, keep learning and keep improving.

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  • 2 min read

Pros and Cons of Event-Driven Architecture

Hello and welcome to another episode of Continuous Improvement. I'm your host, Victor Leung, and today we're diving into a topic that's reshaping how we build and manage software systems: Event-Driven Architecture, or EDA. We'll explore what makes it so powerful and also discuss the challenges it brings. Whether you're a developer, a system architect, or just a tech enthusiast, understanding EDA is crucial in today's digital environment.

Let’s start with the basics. Event-Driven Architecture is a way of designing software systems that revolves around the production, detection, and reaction to events. These events can be anything from a user action, like clicking a button, to automated alerts from a sensor in a manufacturing plant.

The beauty of EDA lies in its scalability. By decomposing systems into event-processing components, each part can scale independently, making it incredibly efficient at handling varying loads.

Another major benefit is the loose coupling it provides. In EDA, components interact primarily through events. They don’t need to know the details of other systems, just what events to listen for. This not only makes systems more flexible but also eases maintenance and updates.

EDA also excels in environments that require high responsiveness. Thanks to its asynchronous nature, systems can process events as they come without waiting on other processes, which is perfect for real-time applications.

And yet, for all its strengths, EDA isn’t without its challenges. The dynamic nature of event-driven systems introduces complexity, particularly when it comes to understanding and managing the flow of events across different parts of the system.

Testing these systems poses unique challenges too. Traditional testing methods might not always work because of the unpredictable nature of event timings and sequences. Developers often need to employ more sophisticated testing techniques to ensure reliability.

And let's not forget about issues with latency and event ordering—particularly in distributed setups where you can have events coming in out of sequence. Managing these effectively requires a robust infrastructure and careful planning.

So, while the benefits of Event-Driven Architecture—like scalability, flexibility, and reactivity—are clear, it’s important to approach implementation with a full understanding of the potential pitfalls. It's about finding the right balance and tools to manage the complexity effectively.

Thank you for joining me on Continuous Improvement. If you’re considering an Event-Driven Architecture for your next project, I hope today’s episode gives you a clearer view of what to expect. Don’t forget to subscribe and share this podcast with your colleagues or friends interested in the latest in tech innovations. Until next time, keep improving and innovating.

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  • 2 min read

Asynchronous Communication with Apache Kafka

Welcome to Continuous Improvement, where we delve into the technologies shaping our future. I'm your host, Victor Leung. Today, we're exploring a key component of modern software architectures: asynchronous communication. And we're taking a closer look at Apache Kafka, a tool that has become essential for managing data streams in distributed systems.

Let's start with the basics. Asynchronous communication—what is it? Unlike synchronous communication, where immediate responses are expected, asynchronous methods allow messages to be sent without waiting for the receiver to be ready. This approach is crucial for efficiency in distributed systems, where waiting for responses could bog down the entire network.

Consider how we communicate daily. Direct messaging expects an instant reply, much like synchronous systems, while email allows us to respond at our convenience, mirroring asynchronous communication. This flexibility is what systems like HTTP AJAX exploit to enhance web interactions, allowing background data loading without disrupting the user experience.

In the world of microservices and distributed architectures, asynchronous communication isn't just an option; it's a necessity. It supports everything from simple message queues to complex event streaming and stream processing.

And that brings us to Apache Kafka. Born at LinkedIn and open-sourced in 2011, Kafka is designed to handle vast amounts of data in real-time. It's not just a messaging queue; it's a comprehensive event streaming platform.

Kafka’s architecture includes several key components. At its heart are the Kafka brokers, which manage the storage and transmission of messages. Producers send messages to Kafka topics, which are then consumed by consumers. What's unique about Kafka is its robust handling of these messages through partitions and topics, ensuring scalable and reliable message delivery.

But Kafka isn't just for the tech giants. Its use cases span industries from banking to manufacturing, helping companies manage real-time data for everything from operational monitoring to predictive analytics.

As we wrap up, remember that embracing technologies like Kafka can transform how we think about data flow and system design. It's about choosing the right tools to build systems that are not only efficient but also resilient and capable of handling the demands of today.

Thank you for tuning into Continuous Improvement. If you're intrigued by how asynchronous communication can benefit your projects, don't forget to subscribe and share this episode. Until next time, keep evolving your systems and your thinking.

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  • 2 min read

Our Future is AI - Choosing the Companion You Want to Live With

Welcome back to Continuous Improvement, where we explore the intersections of technology, innovation, and humanity. I'm your host, Victor Leung. Today, we're venturing into the transformative world of Artificial Intelligence, a force reshaping not just industries but the very fabric of our society. From healthcare to agriculture, AI's potential is immense, but so are the ethical considerations it raises. Let's dive into how we can align AI with our deepest values and needs.

The healthcare sector illustrates AI's promise vividly. Traditional healthcare models are struggling with rising costs and inefficiencies. AI introduces revolutionary capabilities in detecting, diagnosing, and treating diseases, potentially saving billions and, more importantly, saving lives.

But AI's influence doesn't stop with healthcare. Imagine its applications in building maintenance where costs are skyrocketing, or in social care where human resources are stretched thin. AI can bring not only efficiency but also enhanced effectiveness to these critical areas.

Yet, as AI evolves from the Symbolic AI of the 1950s to today's Generative AI, we face new challenges. Generative AI, powered by advanced models like transformers, promises to revolutionize fields with its ability to understand and generate human-like text, yet it also brings concerns about transparency and the reliability of what AI is creating.

And here lies the ethical paradox of AI. The technology that offers to enhance our capabilities also poses risks—risks like job displacement, deepfakes, and even cybercrime. How do we navigate these waters? Regulation is a start, like the EU's AI Act, but it's also about the choices we, as a society, make.

The integration of AI with robotics opens yet another frontier. From agricultural robots that can plant and harvest crops to personal care robots that assist with daily tasks, the potential is staggering. But so is the need for strict safety and ethical standards.

As we stand at this crossroads, the question becomes not what AI can do, but what AI should do. Choosing AI tools and systems that respect privacy, ethics, and personal autonomy is crucial. Our AI companions should enhance our lives without undermining our values.

In conclusion, as we embrace AI's vast potential, let's also champion the values that define us as humans. It's not just about the power of what AI can achieve but also about the wisdom with which we guide it.

Thank you for joining me on Continuous Improvement. If today’s discussion inspired you, don’t forget to subscribe and share this episode. Until next time, let's ensure the future we create with AI reflects the best of who we are.

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  • 2 min read

Embracing the Axioms of Digital Architecture for Transformation

Hello and welcome to another episode of Continuous Improvement. I'm your host, Victor Leung, diving deep into the strategies that shape the digital world. Today, we're exploring the axioms of digital architecture—principles that redefine how businesses should structure their digital strategies to stay agile, resilient, and customer-focused in a rapidly evolving landscape.

Let's kick off with a fundamental shift in perspective—the concept of Outside-In Thinking. Traditional approaches often start by asking what the client needs, but to truly revolutionize customer experience, we need to dig deeper. This means uncovering not just the expressed needs of customers but also their latent desires, using a human-centric design approach. It’s about ensuring our solutions resonate on a deeper level with those who use them.

Moving on, we encounter the necessity of Rapid Feedback Loops. In a world where customer preferences shift at breakneck speeds, continuously validating these needs becomes crucial. By integrating customer feedback early and frequently, businesses can iterate their solutions swiftly, ensuring relevance and efficacy in meeting real-world demands.

Next, let’s talk about the Bias for Change. In the digital realm, change isn’t just inevitable; it's the baseline. Our architectures must not only accommodate but embrace change, maintaining flexibility to adapt without becoming bogged down by it. Think of architecture as a living entity, one that evolves with planned and agile responses to the changing environment.

This leads us to a profound insight from Conway’s Law, suggesting our Organization Mirrors Architecture. The law posits that a system’s design will reflect the organization's communication structure. The Inverse Conway Maneuver advocates for structuring our teams to promote the architecture we aspire to achieve, aligning our organizational dynamics with our architectural goals.

Empowerment within teams is also vital. Autonomous Cross-Functional Teams are the engines of innovation and agility. By empowering teams to make decisions and respond to challenges independently, organizations can address problems more efficiently while staying aligned with broader business objectives.

In terms of system design, the preference for Loosely Coupled Systems cannot be overstated. These systems minimize dependencies between components, enhancing flexibility and resilience—key attributes for maintaining functionality in a dynamic market.

Lastly, we shift focus from traditional Layering to Partitioning. Unlike layering, which often creates rigid, siloed structures, partitioning is about creating a modular and scalable architecture. It's driven by market needs at the business level and capabilities at the operational level, facilitating easier adaptation to new challenges and opportunities.

In conclusion, these axioms of digital architecture provide a roadmap for building digital strategies that are not only robust and scalable but also agile and deeply aligned with customer needs. In the digital era, adopting these principles isn't just beneficial; it's essential for thriving amidst constant change.

Thank you for tuning into Continuous Improvement. We hope today’s discussion inspires you to rethink how your organization approaches digital architecture. For more insights into navigating the digital landscape, make sure to subscribe to our podcast. Until next time, keep innovating and improving, because the best way to predict the future is to invent it.

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  • 2 min read

ISO 20022 - the Global Standard for Financial Messaging

Welcome to Continuous Improvement. I'm Victor Leung, your guide through the intricate world of technology and its impact on our lives. Today, we're delving into a topic that's reshaping the financial sector: ISO 20022. This international standard is revolutionizing the way financial institutions communicate, ensuring that as our financial systems become more global, they also become more interconnected and efficient.

ISO 20022 isn't just another technical jargon; it's a crucial standard for electronic data interchange between financial entities. It covers a broad spectrum of financial business domains, from payments to securities, and even foreign exchange. The goal? To streamline and secure the way financial messages are sent and received worldwide.

So, what makes ISO 20022 stand out? First, it offers a rich data model. This model uses a universal dictionary that defines every piece of financial information in a message, ensuring clarity across different systems and countries.

But it doesn't stop there. ISO 20022 is designed with flexibility in mind, supporting various message formats like XML and JSON, and it’s extensible, meaning new messages can be added without disrupting existing systems. It's like having a universal translator that not only understands everyone's language but also adapts to new dialects as they emerge.

The benefits of adopting ISO 20022 are clear: enhanced efficiency, improved accuracy, and better compliance with regulations like AML and KYC. It's not just about sending messages faster; it's about making them more meaningful and compliant.

Yet, transitioning to ISO 20022 is not without its challenges. Institutions need to overhaul their systems, which means significant investments in technology and training. The move from legacy systems to a standardized format requires meticulous planning to ensure that daily operations aren't just maintained but optimized.

Looking ahead, the future of ISO 20022 is bright. It’s poised to become the global standard for financial messaging as more central banks and payment systems worldwide adopt it. With the digital economy expanding and real-time payment systems becoming the norm, ISO 20022's role is only expected to grow.

In conclusion, ISO 20022 is transforming financial communications, not just improving the backend of transactions but also paving the way for future innovations in the financial industry. It's a testament to how standardized processes can lead to more efficient and secure systems.

Thank you for tuning in to Continuous Improvement. Today, we've explored how ISO 20022 is shaping the financial landscape, ensuring that our global financial infrastructure is not only robust but also future-ready. For more insights into how technology is transforming industries, subscribe to our podcast. Until next time, keep improving and pushing the boundaries of what's possible.

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  • 2 min read

Microsoft Fabric - Revolutionizing Data Analytics in the AI Era

Welcome back to Continuous Improvement. I'm Victor Leung, and in today's episode, we're diving deep into a solution that's reshaping the landscape of data analytics and AI integration—Microsoft Fabric. In a world where data is akin to the lifeblood of AI, managing and utilizing this data effectively is crucial for any organization's success. Microsoft Fabric offers a streamlined approach to this challenge, ensuring that data isn't just collected but is also effectively harnessed.

The rise of disparate tools for data handling—from Hadoop to Spark—has often left CIOs feeling more like Chief Integration Officers. Microsoft Fabric is designed to address this by unifying these diverse systems into a cohesive, integrated stack. Let’s explore how this platform is moving us from fragmentation to unity in the realm of data analytics.

Microsoft Fabric is built on four core design principles that make it a game-changer for businesses. First, it’s a Complete Analytics Platform—unified, SaaS-fied, secured, and governed. This means all your data analytics needs are met under one roof without the hassle of juggling multiple tools.

Secondly, the platform is Lake Centric and Open. At its heart lies the principle of "One Lake, One Copy," which emphasizes maintaining a single data lake that is open at every tier. This not only ensures flexibility but also enhances the openness of your data systems.

Thirdly, Microsoft Fabric aims to Empower Every Business User. With seamless integration into Microsoft 365, the platform is designed to be intuitive and familiar, enabling users to effortlessly turn insights into action.

And lastly, AI Powered. Fabric isn’t just using AI; it embeds generative AI into the platform, enhancing every aspect of data interaction, from analytics to management, ensuring that your decisions are informed by the most intelligent insights available today.

Transitioning from legacy systems like Azure Data Factory to this SaaS-fied experience means that businesses can now enjoy a more streamlined, cost-effective, and scalable approach to data management. Microsoft Fabric essentially acts as the OneDrive for data through its OneLake feature, providing a single, organized, and indexed SaaS lake that simplifies data discovery, governance, and compliance.

Another standout feature of Microsoft Fabric is Copilot, an AI assistant that helps users enrich and analyze data within notebooks. Imagine being able to converse with your data, asking questions, and modeling predictions through a simple dialogue. Copilot makes this possible, enhancing productivity and understanding across your team.

In conclusion, Microsoft Fabric represents not just a technological evolution but a strategic revolution in how we handle data in the digital age. By adhering to its core principles, it promises a unified, flexible, and profoundly intelligent approach to data analytics.

Thank you for joining me on Continuous Improvement as we explored the transformative capabilities of Microsoft Fabric. For more insights into how technology can revolutionize your business processes, make sure to subscribe to our podcast. Until next time, keep pushing the boundaries of what's possible and continue to improve.

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  • 2 min read

A Pragmatic Approach Towards CDK for Terraform

Hello and welcome to Continuous Improvement. I'm your host, Victor Leung, here to explore the latest and greatest in technology tools and trends. Today, we're diving into an exciting development in the world of infrastructure management—specifically, the Cloud Development Kit for Terraform, or CDKTF. This innovative tool leverages the familiar programming languages we use every day to define cloud infrastructure. Whether you're a developer, a system architect, or just a tech enthusiast, this episode will shed light on how CDKTF is changing the game in Infrastructure as Code.

Infrastructure as Code, or IaC, has fundamentally transformed how we provision and manage resources in the cloud. Terraform, by HashiCorp, has been at the forefront of this revolution, allowing teams to manage their infrastructure through declarative configuration files. However, the introduction of CDK for Terraform is set to take this a step further by integrating the power of programming languages like TypeScript, Python, Java, C#, and Go.

CDK for Terraform is built on top of the AWS Cloud Development Kit and uses what's called the JSII, or JavaScript Interop Interface, which allows publishing of constructs that are usable across these languages. This polyglot approach not only broadens the accessibility of Terraform but also enhances the flexibility in how infrastructure can be defined and managed.

Let's break down the building blocks of CDKTF:

  • The App Class is where you initialize your CDK application; it's the starting point of your infrastructure configuration.
  • The Stack Class represents a collection of related resources that are deployed together as a unit.
  • The Resource Class encompasses individual infrastructure components—think of things like your EC2 instances or S3 buckets.
  • And finally, Constructs. These are the bread and butter of CDK apps, encapsulating logic and forming the basis of higher-level abstractions.

Now, when should you consider using CDK for Terraform? Here are a few scenarios: - If your team prefers procedural languages over learning a new domain-specific language, CDKTF is a great choice. - For complex infrastructures that benefit from higher-level abstractions, CDKTF allows you to create reusable constructs that simplify management. - And if your team is on the cutting edge and ready to adopt new tools, even if they might still be evolving, CDKTF offers a dynamic approach to infrastructure management.

In conclusion, CDK for Terraform provides a pragmatic way to apply familiar programming skills to cloud infrastructure management. It's about streamlining processes and making technology work smarter for us. As with any tool, it's crucial to assess whether CDKTF fits your project's needs and your team's readiness for new technologies.

Thank you for joining me today on Continuous Improvement. I hope this discussion on CDK for Terraform has inspired you to explore new tools and perhaps rethink how you manage your infrastructure. Don't forget to subscribe for more insights into how technology can improve and simplify our workflows. Until next time, keep innovating, keep improving, and let's make technology work for us.