https://www.youtube.com/watch?v=Ft0qY55Rsqw
Joe Reis joins us to reflect on life after Fundamentals of Data Engineering, what makes data content worth consuming, and why good taste matters as much as technical skill. We talk about burnout in big tech, the myth of AI replacing everyone, and how Discord communities, DJ sets, and a sense of humor are helping shape the future of data. This one’s part industry pulse check, part real talk.
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http://youtu.be/0xj2-PRX2R8
Barzan Mozafari, CEO of Keebo and former computer science professor, joins us to explore how AI is changing the way data teams work. We talk about the hidden inefficiencies in cloud data platforms like Snowflake and Databricks, how reinforcement learning can automate performance tuning, and why the tradeoff between cost and speed isn’t always what it seems. Barzan also shares thoughts on LLMs, the future of conversational analytics, and what data teams should (and shouldn’t) try to build themselves.
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Keebo – https://keebo.ai/
What if your data pipeline could run 10x faster without the overhead? Jacopo Tagliabue, CTO of Bauplan and NYU adjunct professor, is pushing the boundaries of data infrastructure with lightweight DAGs, Apache Arrow, and a radically different take on functions as a service. In this episode, he breaks down the tech stack behind Bauplan and why the future of scalable data pipelines is all about speed, modularity, and zero-copy design.
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Bauplan – https://www.bauplanlabs.com/
Sol Rashidi has built AI, data, and digital strategies inside some of the world’s biggest companies—and she’s seen the same mistakes play out again and again. In this episode, she unpacks why AI initiatives often stall, how executives misread what “transformation” really requires, and why the future of AI success isn’t technical—it’s cultural. If you think AI is just a tech problem, Sol is here to change your mind.
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Learn more about Your AI Survival Guide by Sol Rashidi
This report helps data leaders guide their teams to architect such pipelines. We define must-have characteristics, explore compelling use cases and provide guiding principles for success.
In this complimentary copy of Modern Data Streaming for Real-Time Artificial Intelligence, you’ll discover:
Spencer Cook, Lead Solutions Architect at Databricks, joins to unpack how enterprises are moving beyond hype and building practical AI systems using vector search, RAG, and real-time data pipelines. He and John Kutay get into what it really takes to serve production LLMs safely, avoid hallucinations, and tie AI back to business outcomes—without losing sight of governance, latency, or customer experience.
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The General Medical Council (GMC) is the independent regulator of doctors in the United Kingdom, responsible for protecting patient safety and upholding standards in medical practice. Established in 1858, the GMC maintains the official register of medical practitioners, ensures the quality of medical education and training, and investigates complaints about doctors’ conduct and performance. Its regulatory duties span the full medical career lifecycle—from medical school accreditation to post-graduate training oversight and fitness to practise tribunals—making it a cornerstone of the UK’s healthcare system.
With over 1,700 employees supporting more than 300,000 registered doctors, the GMC depends on timely, accurate, and secure data to fulfill its mission. Its work involves sensitive and complex data, including personal identifiers, legal casework, and educational records. As the organization modernizes its infrastructure, the move toward real-time, cloud-based analytics is essential for faster reporting, enhanced transparency, and future-ready capabilities like AI-driven insights. This transformation enables GMC to deliver more responsive regulation and support high-quality care across the UK.
GMC’s strategic goal was to migrate to a modern, cloud-based analytics stack built around Azure and Power BI. But there was one major obstacle: their primary data source, Siebel CRM, wasn’t ready to move to the cloud.
The organization faced several limitations:
Following a thorough review of how the right data was critical in the right architecture, it was shared:
To solve this challenge, GMC needed a real-time integration layer that could stream on-prem data to Azure reliably. After evaluating several solutions—including Oracle GoldenGate and Qlik—they selected Striim for its:
GMC’s team worked with Striim to deploy a streaming solution that connected their Siebel source data to the cloud—while simultaneously scaling up their Azure environment. The implementation helped the team build out its new architecture while laying the groundwork for broader real-time data access.
Even before completing their full migration, GMC saw significant operational benefits:
✅ Cost Savings
By retiring Tableau (an estimated £90,000/year) and planning the decommissioning of Oracle analytics and Informatica, GMC reduced analytics costs while positioning the organization for scalable growth.
✅ Faster Back-End Operations
Previously, if an ETL failed, re-running it meant uploading over 150 GB of data—a process that could take hours and disrupt business operations. With Striim’s live streaming in place, data is always current, and ETLs can be triggered on demand.
✅ Minimal Disruption
Because Striim runs in parallel with existing ETLs, GMC was able to phase in their new system gradually, minimizing risk during the transition.
✅ Strategic Flexibility
Striim enabled decoupling from legacy infrastructure, empowering GMC to scale up Power BI adoption and build out its modern cloud analytics stack with confidence.
By connecting on-prem systems with Azure in real time, GMC is not only solving today’s data integration challenges but also laying the groundwork for tomorrow’s AI, analytics, and compliance initiatives.
Looking ahead, GMC’s analytics roadmap includes:
GMC’s transformation highlights the power of real-time data integration in modernizing legacy systems and enabling a cloud-first future. Striim delivered the scalability, compliance, and speed needed to help GMC accelerate its journey while keeping costs in check and teams empowered.
Want to see what Striim can do for your organization?
Today’s businesses generate and collect vast amounts of data from an ever-growing array of sources—transactional databases, customer relationship management (CRM) systems, website interactions, social media platforms, IoT devices, and more.
However, integrating and harmonizing these disparate data streams in real time presents a formidable challenge. The complexity arises from differences in data formats, structures, latency requirements, and the need for seamless orchestration between multiple systems. Without a unified approach, businesses struggle to gain a holistic view of customer behaviors, leading to missed opportunities, disjointed experiences, and inefficiencies in decision-making.
Real-time data streaming addresses these challenges head-on by serving as the backbone of real-time AI data pipelines. Its distributed, in-memory streaming architecture ingests, processes, and integrates unbounded and evolving data streams with unmatched efficiency and minimal latency. Striim seamlessly connects diverse data sources, applies transformation and enrichment in real time, and delivers unified, actionable insights across AI, BI, and operational platforms.
Specifically, Striim’s AI-ready architecture goes beyond traditional integration by enabling businesses to:
The integration of Generative AI (GenAI) into real-time data pipelines enables businesses to analyze and respond to customer behaviors dynamically. With GenAI, organizations can:
By processing diverse data sources in real time, businesses gain immediate insights into customer preferences, intent, and engagement. This enables:
GenAI allows businesses to craft highly customized experiences by dynamically analyzing individual customer data. With real-time AI-driven insights, organizations can:
Consumer expectations shift rapidly, and static models quickly become obsolete. Striim enables businesses to adapt their AI models dynamically by:
Businesses leveraging real-time data with GenAI achieve higher engagement levels by:
Striim’s platform is designed with advanced capabilities that bridge real-time data integration and AI-driven analytics. Key technical differentiators include:
Striim ingests data from various sources—transactional systems, IoT devices, clickstreams, CRM platforms—leveraging low-latency messaging frameworks like Apache Kafka and MQTT. The distributed in-memory architecture ensures high throughput and efficient handling of real-time workloads.
Striim natively supports GenAI models, enabling real-time execution of:
With built-in support for:
Striim ensures AI-powered insights reach the right touchpoints at the right time:
Organizations that harness real-time data and GenAI with Striim unlock transformative outcomes:
The future of customer engagement is real-time, AI-powered, and insight-driven. Businesses can no longer afford to operate on fragmented, delayed data streams. Striim unifies diverse data sources, integrates AI seamlessly, and delivers real-time intelligence that transforms customer interactions.
By merging operational and behavioral data streams with AI-enhanced analytics, Striim empowers enterprises to stay ahead of the curve—ensuring every customer experience is timely, relevant, and impactful.
Striim is the backbone of modern AI-driven enterprises, providing the real-time data infrastructure needed to drive intelligent automation, adaptive customer engagement, and sustained business growth.
Real-time data has become a non-negotiable foundation for powering machine learning (ML) and generative AI (GenAI). From delivering event-driven predictions to powering live recommendations and dynamic chatbot conversations, AI/ML initiatives depend on the continuous movement, transformation, and synchronization of diverse datasets across clouds, applications, and databases.
But complexity stands in the way: incompatible platforms, brittle pipelines, fragmented architectures, and the growing pressure of data privacy and compliance risks make it challenging for teams to deliver trusted, real-time data to models and applications.
In this webinar with BARC, a leading analyst firm for data & analytics and enterprise software, you’ll learn how to overcome these challenges and build the data backbone for AI/ML success.
Join us to:
Break down the key elements of modern data streaming architectures and their impact on AI/ML.
Define the must-have characteristics of a data streaming architecture.
Explore real-world use cases, including streaming ELT for experimentation, real-time ML, and retrieval-augmented generation (RAG) for GenAI.
Gain actionable guidance to build scalable, resilient streaming pipelines that drive continuous innovation and measurable value.
Executives and practitioners leading data and AI transformation — learn what it takes to stay competitive. Register today!
AI and analytics have the potential to transform decision-making, streamline operations, and drive innovation. But they’re only as good as the data they rely on. If the underlying data is incomplete, inconsistent, or delayed, even the most advanced AI models and business intelligence systems will produce unreliable insights.
Many organizations struggle with:
These issues don’t just affect technical teams—they impact every aspect of the business, from customer experience to operational efficiency. Without high-quality, available data, companies risk misinformed decisions, compliance violations, and missed opportunities.
To extract meaningful value from AI and analytics, organizations need data that is continuously updated, accurate, and accessible. Here’s why:
Many businesses are shifting toward real-time data pipelines to ensure their AI and analytics strategies are built on reliable information. Here’s how they are tackling these issues:
Rather than storing data in isolated systems, organizations are adopting real-time data integration strategies to unify structured and unstructured data across databases, applications, and cloud environments.
Modern data architectures incorporate automated validation, cleansing, and enrichment techniques to detect missing values, inconsistencies, and errors before they reach AI and analytics platforms.
To avoid delays, businesses are implementing streaming data platforms that allow information to be processed as soon as it is generated, rather than relying on batch updates.
With growing regulations around data privacy, organizations must enforce real-time lineage tracking, access controls, and encryption to ensure sensitive data remains secure.
AI models require ongoing updates to stay relevant. Leading companies are using continuous learning techniques to keep AI applications accurate by feeding them real-time, high-quality data.
Striim helps organizations solve these challenges by ensuring real-time, clean, and continuously available data for AI and analytics. With low-latency streaming, automated data validation, and AI-powered transformations, Striim enables businesses to:
By solving the data quality and availability problem, Striim helps businesses unlock AI’s full potential—ensuring that decisions are driven by accurate, real-time intelligence.
The success of AI and analytics depends on how well businesses manage data quality and availability. Companies that fail to address these challenges risk acting on faulty insights, missing market trends, and losing their competitive edge.
By investing in real-time, high-quality data pipelines, organizations can ensure that AI and analytics initiatives deliver accurate, timely, and actionable intelligence.
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