Tag: integrations

  • We Will Control the World!

    We Will Control the World!

    One thing about centralizing control is that there always has to be someone in charge. That can be a very good thing or a very bad thing. In IT, it doesn’t even get far enough to be a terrible thing.

    Generative AI and increasing integrations have renewed the insistence from the crowd that it is certain they should control every little thing in your (virtual) data center. The idea that a given vendor should be the configuration and management point for a wide variety of tools – their own, competitors and nearby market tools – has been around forever. Vendors love the idea that they have that level of input into your infrastructure and, let’s be honest, many of us love the idea of having it taken care of for us.

    But there are several questions to ask here. First, what is the likelihood that vendor X will have a great level of support for the tools from other vendors in your infrastructure? I mean, before we even get into the idea that this desire for control is not at all altruistic (we’re not talking Stalin here, but we certainly aren’t talking Washington, either), there is the question of how AI will be able to learn about and configure/manage a competitor’s products. Watching wire traffic? Well, if the competitor’s systems are super-well-known and the format of configuration APIs/files are well known—maybe? But this leads us to the real problem.

    My advice to vendors would be to get their own products under control first. We work in a high-complexity field where the tools require a lot of specific domain knowledge to manage, and even more to manage well. Vendors have generally taken for granted that this level of knowledge would be available. Before making that level of knowledge unnecessary for competitors or spiderwebbing into competitor installs, make your tool(s) damn simple to use. That doesn’t mean you have to simplify it—you have the power of AI and are going to focus on configuration and management. So nearly eliminate configuration issues with your own products.

    Example: “Great Security Tool, I just added an API at https://devopsy.kinsta.cloud/rocks. Please lock it down so it is accessible only to users in the WeRule group, then generate a WAF rule to keep it from being used to access more than three records a day, and only allow GET and POST calls.” Most vendors across the industry aren’t even close to this level of simplicity, so why in the world would you trust them to configure/control other vendor’s products?

    There is a trend to make products cover more of the space they are in. This is very big in security right now, but you also see it in DevOps. CI/CD/CDD/VC/kitchen sink (KS)—I’m okay with that, as it does offer us simplified configuration and better cross-app communication. I think this is a better route than trying to serve as command-and-control for an entire tech stack that the vendor does not own. I still want point products and best-of-breed available, but a lot of us just don’t have the time and resources to implement five different products to get the job done—even if they’re “free,” as we all know, they are not free—so one vendor doing an entire chain is great for that use case, and often the shared information about the app, security, the network, etc. can work together better than the sum of its parts. So let’s stay in the “umbrella app” lane and not get carried away with trying to drive in the “manager of the entire world” lane.

    We’re busy, we’re tired and many of us are jaded. So don’t tell us how you can manage other vendors’ tools, tell us how you’re making it easier to manage yours. Thanks, from all of us in IT.

  • Boomi Brings Generative AI to Integration Platform

    Boomi Brings Generative AI to Integration Platform

    Boomi announced today it is applying generative artificial intelligence (AI) to enable organizations to automate integrations via a natural language interface.

    Boomi CEO Steve Lucas said Boomi AI makes it much simpler for professional and citizen developers alike to create integrations at scale using generative AI platforms to invoke the low-code engine that Boomi already provides to create integrations between thousands of applications.

    Those generative AI capabilities will complement a wide range of AI capabilities based on machine learning algorithms that are already embedded in the Boomi platform, he added.

    Generative AI’s Impact on Integration and Automation – Steve Lucas, Boomi from Techstrong.TV on Vimeo.

    Boomi has been widely used to drive business process integration via an integration platform-as-a-service (iPaaS) environment it delivers as a managed service. The low-code platform the company created significantly reduces the need to rely on professional developers to write procedural code using Java, for example, to create those integrations.

    Generative AI capabilities make it possible for almost anyone of any skill level to create those integrations across multiple types of application programming interfaces (APIs), noted Lucas.

    That capability also makes it possible for Boomi to notify end users when, for example, a data attribute in an application has changed and ask them if they would like to apply that change across their entire application environment.

    In addition, Boomi AI can also interpret the intent of a prompt to better orchestrate processes in addition to providing an audit history in natural language that explains how decisions were made and what outcomes occurred as a result. As part of that effort, Boomi algorithms are also trained to avoid biases and unfairness with established ethical guidelines.

    Boomi’s AI engine is trained using anonymized data collected from approximately 20,000 customers that have created more than 200 million integrations using the Boomi platform. Those integrations have been exposed to third-party large language models (LLMs) that the company is using to train Boomi AI, said Lucas. Boomi is not storing any customer data to train its AI models.
    DevOps teams, of course, spend hours creating integrations that should be much simpler to create, test and validate with the rise of generative AI. In fact, the number of integrations being created to drive a wide range of digital business transformation initiatives should substantially increase.

    In the meantime, DevOps teams would be well-advised to start evaluating which tasks they manually perform today that could instead be automated by a generative AI platform. Many of the routine integrations that DevOps teams create today using low-level APIs will be increasingly automated by end users. The ultimate goal should be to identify more complex tasks that previously DevOps teams would not have attempted simply because there wasn’t enough time.

    It’s still too early to determine what impact generative AI will have on the way IT is managed, but DevOps teams that are typically committed to automating as many processes as possible should be at the forefront of adoption. The challenge is going to be simply keeping track of the rate of change that might soon be enabled by these platforms.

  • How Embedded Analytics Can Transform Future Businesses

    How Embedded Analytics Can Transform Future Businesses

    It’s no secret that big tech companies like Google, Amazon and Netflix are not only leveraging data but also delivering embedded analytics and insights as a standard functionality of their software, services, applications and toolkits. The analytics appear to consumers as recommendations, usage habits and other helpful information. Such insights not only help consumers extract the most value out of the product they are using but also help providers understand how they can provide better services. Businesses that embed analytics to provide a differentiated user experience also have a direct impact on engagement and revenue.

    Have you considered how embedded analytics could differentiate your company’s own software and hardware products? Let’s understand how embedded analytics can help transform technology businesses:

    1. Analytics are expected and welcomed in your products

    By embedding analytics into existing enterprise software and applications, users can drill down into real-time and context-aware data (for example: How busy a location will be at various times of the day) and can benefit from faster data-driven decision-making without worrying about application compatibility. Commonly, software applications provide in-app analytics to delight and serve their customers, while hardware companies can provide helpful stats on a web portal or app.

    2. Embedded analytics is easier than ever today

    Cloud technology has completely disrupted tech-enabled industries. It’s easier than ever for software, apps and hardware to connect to a data repository to save logs that are a rich source of analytics. The invention of the S3 bucket and object store has made it fairly cheap to keep log data. Finally, the transition to platform-as-a-service (PaaS) and software-as-a-service (SaaS) has fundamentally added an “easy button” to tracking consumption patterns and resource utilization.

    3. Embedded analytics helps design better products and services

    Embedded analytics can give businesses more insight into application telemetry (how customers use the data, how they engage with the product) and provide insights into the challenges they face from a usability standpoint. Providers can use advanced analytics to quickly iterate, draw patterns and make predictions, troubleshoot customer challenges, discover new avenues to monetize, detect and mitigate security threats or create entirely new features and applications that put customers on a faster path to improved application experience and better decision-making.

    4. Embedded analytics increases operational efficiencies

    The tech industry is transitioning from traditional monolithic architectures where applications are self-contained to microservices architectures where different components are spun up as needed via API calls. By embedding analytics directly into developer tools, businesses can manage resources without sacrificing availability or performance. Companies can allocate and optimize resources dynamically and understand peaks and valleys in consumption. Businesses must be able to leverage historical and real-time data to build predictive models to proactively identify and mitigate issues with individual components of the application before the customer experiences a problem.

    Embedding Analytics in Your Application

    Before you start embedding analytics in your application, it’s important to choose the right database foundation, as this will support current needs and future goals. Below are the top capabilities that should be standard features in analytics databases:

    1. Handles massive data volumes: The database must scale easily and efficiently with minimal cost using massive parallel processing. Today’s scale might be terabytes, tomorrow it might be petabytes and so on.

    2. Enables speedy data analytics: Speed is a key factor in a positive user experience. Users want instant insights because they don’t like waiting for results. The database should be capable of optimizing load and query performance so that analytics is both timely and relevant.

    3. Embeds machine learning: Machine learning algorithms are critical for predictive analytics. A platform that can embed ML and iteratively learn from new data can make a big difference and add a whole new level of intelligence.

    4. Features native integrations: The more time developers spend figuring out tools and integrations, the less time is spent innovating. The platform must ideally have out-of-the-box integrations with popular applications, data sources (on-premises, cloud, multi-cloud or hybrid), ETL, and visualization tools (e.g., Microstrategy, Looker, Power BI, Qlik, Tableau). It should be flexible enough to support user-defined functions for things that don’t have native integration.

    5. Encourages self-service: The database should make it easy for data scientists to leverage tools and programming languages they already know such as R, Java, C/C++, Python and SQL. Data scientists will also appreciate a platform that has built-in functions for a wide range of analytics (such as geospatial, time-series, event-series, real-time, machine learning, text analytics, pattern matching and regression) and delivers the tools to spot trends, uncover anomalies and anticipate the unexpected.

    6. Supports highly advanced analytics: Many platforms can analyze single tables or do a simple look-up but few have the power to analyze dozens of tables, hundreds of data types, dimensions and attributes from numerous sources over several years of history.

    7. Unified: The database should be able to streamline everything right from data ingestion to exploration to production until delivery. Users should be able to analyze data in its place to always have a single source of truth.

    It’s not hard to visualize that the most disruptive companies of the future will be the ones that harness data as a core asset and embed analytics in their products and services. Embedding analytics requires the right database foundation; to get to the right foundation, IT teams need to cut through the noise and study the fine print. With the proper database, the future is yours to create.