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Vendors in the news

27 vendors this week · click a name to expand
Community subreddits: r/dataengineering · r/datascience · r/analytics · r/BusinessIntelligence · r/MachineLearning · r/LocalLLaMA · r/SQL
AWS 4Alteryx 1Amazon 3Apache 1Azure Data Factory 1Basis 1C3.ai 1Clay 1Confluent 1Databricks 5Dataiku 1Eviden 1Exa Labs 1Fivetran 2Google Cloud 3Ingent 1Microsoft 1OpenAI 2Power BI 1RapidMiner 1Redpanda 1Roche 1SAP 3SAS 1Snowflake 1ThoughtSpot 1dbt 1
AWS 4 stories
Related on Reddit: r/aws · search "AWS" ↗
NewsAgentic AI, MCP & the context layerAWS
AWS Big Data Blog · Read source ↗

OpenSearch Agent Health offers a method to observe and evaluate AI agents in production by integrating with AWS. The process involves deploying the agent along with its observability pipeline on AWS, and then using Agent Health to trace operations and conduct evaluations for ongoing quality improvements.

MyDataWork POV — For those managing AI agents in production, OpenSearch Agent Health is a solid addition to your toolkit. It’s not about flashy dashboards, but about real-time insights and actionable evaluations. However, if you're not already neck-deep in AWS or wrangling agents every day, this might just be noise. The feature set is razor-focused on continual improvement, which is a blessing for teams needing to fine-tune their agents regularly but irrelevant for casual users or those in less dynamic environments.
See how MyDataWork relates to this item
For MyDataWork users, Agent Studio’s pre-deployment scope can seamlessly align with OpenSearch Agent Health's post-deployment evaluation. Use Agent Studio to ensure the right agentic use case is selected, and then rely on OpenSearch for ongoing quality checks. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
AnalysisData engineering & the warehouse/lakehouseConfluentAWSRedpanda
tech-insider.org · Read source ↗

The comparison between Confluent Cloud, AWS MSK, and Redpanda Cloud in 2026 highlights their respective strengths and weaknesses in cloud-based data streaming. Confluent Cloud is known for its robust ecosystem and integration capabilities, AWS MSK emphasizes seamless AWS service integration, while Redpanda Cloud offers unique efficiency and low-latency advantages.

MyDataWork POV — If you're juggling massive streaming data pipelines, Confluent Cloud's rich ecosystem could be your ally, offering integration ease that might just simplify your life. But if your world revolves around AWS, you'll appreciate AWS MSK for its seamless service tie-ins, though be ready to navigate its complexity. Redpanda Cloud, however, is the dark horse with its efficiency and low-latency perks—ideal for those ready to step outside giant shadows for performance gains. It's not about picking a winner; it's about finding your fit.
Discussion happens on Reddit — no comments are hosted here.
Case studyData engineering & the warehouse/lakehouseAWSApache
AWS Big Data Blog · Read source ↗

AWS Glue 6.0 introduces the ability to build real-time, near-real-time, and batch data pipelines on a single platform. It leverages Spark Real-Time Mode to flag high-risk trades with sub-second latency and uses Apache Iceberg v3 for storing heterogeneous pricing vectors. Additionally, it supports batch analytics with Arrow-native UDFs.

MyDataWork POV — AWS Glue 6.0's real-time capabilities demand attention from data professionals. The integration of Spark Real-Time Mode for sub-second latency in flagging high-risk trades represents a strategic advantage. This focuses on smarter decisions, not faster data. Apache Iceberg v3's support for heterogeneous pricing vectors adds depth, allowing for nuanced financial analysis. While batch analytics with Arrow-native UDFs enhances the offering, it's the real-time element that transforms risk management dynamics. This week, AWS Glue 6.0 is the toolkit to watch.
Discussion happens on Reddit — no comments are hosted here.
NewsData engineering & the warehouse/lakehouseAWS
AWS Big Data Blog · Read source ↗

AWS has introduced a method to enhance Apache Spark debugging on Amazon EMR by using the AWS DevOps Agent. This approach involves registering the Apache Spark Troubleshooting Agent as a custom MCP capability, allowing users to diagnose failing Spark jobs from CloudWatch alarms to pinpointed line-numbered root causes in a single chat session.

MyDataWork POV — Drowning in the complexities of managing sprawling EMR clusters? This DevOps Agent tweak is your new best friend. It bridges the yawning gap between generic alarms and actionable insights, turning CloudWatch's noise into a coherent narrative of failure. But if your Spark jobs are few and far between, this might be a tool that gathers more dust than data. It's the difference between a scalpel for a surgeon and a paperweight for a desk.
Discussion happens on Reddit — no comments are hosted here.
Alteryx 1 story
Related on Reddit: r/alteryx · search "Alteryx" ↗
AnalysisAI platforms — data science & MLRapidMinerAlteryx
TechRepublic · Read source ↗

TechRepublic compares RapidMiner and Alteryx, two prominent data science platforms, evaluating their features, usability, and performance. The article aims to help organizations decide which software better fits their data analysis needs.

MyDataWork POV — The head-to-head comparison of RapidMiner and Alteryx reveals a deeper concern: the growing complexity of data science tools that can overwhelm teams. While both platforms offer powerful capabilities, the risk lies in their steep learning curves and the potential for underutilization. Without adequate training and strategic alignment, organizations may find themselves with sophisticated tools that remain largely untapped, leading to wasted resources and missed opportunities.
Discussion happens on Reddit — no comments are hosted here.
Amazon 3 stories
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AnalysisData engineering & the warehouse/lakehouseAmazon
AWS Big Data Blog · Read source ↗

Amazon OpenSearch Service introduces tools like User Behavior Insights (UBI) data and Search Relevance Workbench (SRW) to help teams assess and improve search result relevance.

Discussion happens on Reddit — no comments are hosted here.
Product launchData engineering & the warehouse/lakehouseAmazon
AWS Big Data Blog · Read source ↗

Amazon Kinesis Data Streams now offers the ability to create streaming tables, which continuously deliver streaming data as queryable Apache Iceberg tables on Amazon S3. This new feature claims to reduce delivery costs by up to 25% and query costs by up to 30% through inline compaction, without requiring any infrastructure management.

MyDataWork POV — Managing large-scale data flows in a cloud-native environment presents challenges that make this development worthy of attention. The integration of Kinesis Data Streams with Apache Iceberg tables offers a streamlined approach to real-time analytics, reducing both delivery and query costs significantly. However, if your data operations are more static or don't rely on real-time processing, you can safely continue with your current setup without missing out on critical improvements.
Discussion happens on Reddit — no comments are hosted here.
Product launchData engineering & the warehouse/lakehouseAmazon
AWS Big Data Blog · Read source ↗

Amazon Redshift now allows integration with AWS IAM Identity Center for authentication on clusters and workgroups using enhanced VPC routing. This setup enables single sign-on with corporate credentials while ensuring all authentication traffic remains within the AWS private network.

MyDataWork POV — This development marks a significant step forward for data teams operating within AWS ecosystems. By streamlining authentication processes with IAM Identity Center, it reduces the friction often associated with managing multiple credentials. Keeping authentication traffic within the AWS private network not only enhances security but also aligns with the broader trend of creating seamless, interlocking data environments. It's a reminder that the strength of enterprise AI lies in the thoughtful connection of diverse components, not in monolithic solutions.
Discussion happens on Reddit — no comments are hosted here.
Apache 1 story
Related on Reddit: search "Apache" ↗
Case studyData engineering & the warehouse/lakehouseAWSApache
AWS Big Data Blog · Read source ↗

AWS Glue 6.0 introduces the ability to build real-time, near-real-time, and batch data pipelines on a single platform. It leverages Spark Real-Time Mode to flag high-risk trades with sub-second latency and uses Apache Iceberg v3 for storing heterogeneous pricing vectors. Additionally, it supports batch analytics with Arrow-native UDFs.

MyDataWork POV — AWS Glue 6.0's real-time capabilities demand attention from data professionals. The integration of Spark Real-Time Mode for sub-second latency in flagging high-risk trades represents a strategic advantage. This focuses on smarter decisions, not faster data. Apache Iceberg v3's support for heterogeneous pricing vectors adds depth, allowing for nuanced financial analysis. While batch analytics with Arrow-native UDFs enhances the offering, it's the real-time element that transforms risk management dynamics. This week, AWS Glue 6.0 is the toolkit to watch.
Discussion happens on Reddit — no comments are hosted here.
Azure Data Factory 1 story
Related on Reddit: search "Azure Data Factory" ↗
CommunityData engineering & the warehouse/lakehouseAzure Data FactoryDatabricks
r/dataengineering · Read source ↗

A consulting company is working with a client to modernize their data environment. The client currently extracts data from on-premises databases like Oracle, SQL Server, and Postgres using Azure Data Factory (ADF) to transfer it into Azure Data Lake Storage (ADLS), followed by processing with Databricks.

MyDataWork POV — The notion of 'modernizing' by simply moving from on-prem to cloud raises more questions than it answers. What's missing is clarity on how this transition tackles data governance, latency, or even cost-effectiveness. Azure Data Factory and Databricks are potent tools, but are they the right fit for every legacy system being uprooted? Without dissecting the specific constraints and ambitions of the current workflow, this 'cloud-first' mantra feels like a glossy overcoat on a yet-to-be-proven structure.
Discussion happens on Reddit — no comments are hosted here.
Basis 1 story
Related on Reddit: search "Basis" ↗
Case studyAgentic AI, MCP & the context layerBasisClayExa Labs
OpenAI Blog · Read source ↗

Basis, Clay, and Exa Labs are leveraging AI agents to enhance workflows in areas like onboarding, account management, and developer integrations. These companies aim to demonstrate how AI-native operations can be applied within enterprises.

MyDataWork POV — The appeal of AI-native workflows is clear, yet there's a significant risk in depending too heavily on AI agents for essential tasks like onboarding and account management. These tasks demand a nuanced understanding of human interaction and context, which AI often struggles to replicate. The risk is that organizations might sacrifice depth and personalization for efficiency, leading to a hollowing out of essential human elements in customer relationships.
See how MyDataWork relates to this item
Agent Studio users should be wary of assuming that AI agents can fully replace human judgment in workflows. Instead, they can use Agent Studio to carefully scope where AI can genuinely add value without undermining the human touch, ensuring a balanced approach. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
C3.ai 1 story
Related on Reddit: search "C3.ai" ↗
AnalysisAI platforms — data science & MLC3.ai
TradingView · Read source ↗

C3.ai is set to report its Q2 earnings, with analysts and investors closely watching the company's performance and guidance. The focus is on how C3.ai's AI solutions are translating into revenue growth and market traction.

MyDataWork POV — The forthcoming earnings report from C3.ai serves as more than a mere financial milestone; it acts as a litmus test for the viability of AI-driven business models. With AI hype at an all-time high, the key issue is whether C3.ai can show actual value creation. Investors should scrutinize both revenue figures and the adoption rates of their AI solutions. If C3.ai can showcase real-world impact, it might finally bridge the gap between AI potential and practical application. This focuses on proving AI's worth in the enterprise, beyond mere numbers.
Discussion happens on Reddit — no comments are hosted here.
Clay 1 story
Related on Reddit: search "Clay" ↗
Case studyAgentic AI, MCP & the context layerBasisClayExa Labs
OpenAI Blog · Read source ↗

Basis, Clay, and Exa Labs are leveraging AI agents to enhance workflows in areas like onboarding, account management, and developer integrations. These companies aim to demonstrate how AI-native operations can be applied within enterprises.

MyDataWork POV — The appeal of AI-native workflows is clear, yet there's a significant risk in depending too heavily on AI agents for essential tasks like onboarding and account management. These tasks demand a nuanced understanding of human interaction and context, which AI often struggles to replicate. The risk is that organizations might sacrifice depth and personalization for efficiency, leading to a hollowing out of essential human elements in customer relationships.
See how MyDataWork relates to this item
Agent Studio users should be wary of assuming that AI agents can fully replace human judgment in workflows. Instead, they can use Agent Studio to carefully scope where AI can genuinely add value without undermining the human touch, ensuring a balanced approach. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Confluent 1 story
Related on Reddit: search "Confluent" ↗
AnalysisData engineering & the warehouse/lakehouseConfluentAWSRedpanda
tech-insider.org · Read source ↗

The comparison between Confluent Cloud, AWS MSK, and Redpanda Cloud in 2026 highlights their respective strengths and weaknesses in cloud-based data streaming. Confluent Cloud is known for its robust ecosystem and integration capabilities, AWS MSK emphasizes seamless AWS service integration, while Redpanda Cloud offers unique efficiency and low-latency advantages.

MyDataWork POV — If you're juggling massive streaming data pipelines, Confluent Cloud's rich ecosystem could be your ally, offering integration ease that might just simplify your life. But if your world revolves around AWS, you'll appreciate AWS MSK for its seamless service tie-ins, though be ready to navigate its complexity. Redpanda Cloud, however, is the dark horse with its efficiency and low-latency perks—ideal for those ready to step outside giant shadows for performance gains. It's not about picking a winner; it's about finding your fit.
Discussion happens on Reddit — no comments are hosted here.
Databricks 5 stories
Related on Reddit: r/databricks · search "Databricks" ↗
Product launchAgentic AI, MCP & the context layerDatabricks
Databricks Blog · Read source ↗

Databricks has introduced the Big Book of AgentOps, a comprehensive guide detailing the operating discipline for building and deploying agentic systems. The publication aims to provide users with best practices and frameworks for managing agents within enterprise environments.

MyDataWork POV — Databricks' foray into AgentOps raises more questions than it answers. The 'operating discipline' sounds promising, but where's the proof that enterprises can actually operationalize these frameworks? It's one thing to publish a book and quite another to ensure it translates into actionable strategy on the ground. Until we see case studies or evidence of successful deployment, it's just another ambitious attempt at standardization in a field notorious for its complexity and variability.
See how MyDataWork relates to this item
Agent Studio could help MyDataWork users define agent use cases before diving into operationalizing them, ensuring that they only pursue viable projects grounded in metadata-driven planning. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Case studyData engineering & the warehouse/lakehouseDatabricks
Databricks Blog · Read source ↗

Databricks engineers managed to cut $1 million annually in AI agent expenses by optimizing their use of these tools. The team focused on identifying inefficiencies in their AI agent deployment, which led to significant cost savings.

MyDataWork POV — The impressive $1 million savings achieved by a leading data platform serves as a wake-up call for every data team overwhelmed by AI agent costs. The real takeaway here is the speed—one hour to uncover and eliminate inefficiencies. This focuses on eliminating unnecessary agent deployments, not on taking shortcuts. It's a reminder that sometimes the most impactful optimization comes not from new tech, but from scrutinizing how we use what we already have.
See how MyDataWork relates to this item
MyDataWork users could benefit from the Use Case Recommendations feature to identify similar inefficiencies and potential cost savings in their AI deployments, ensuring resources are used effectively. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Case studyData engineering & the warehouse/lakehouseDatabricks
Databricks Blog · Read source ↗

The FDA is partnering with Databricks to develop a secure, AI-ready data platform tailored for government use. This initiative aims to modernize the federal data infrastructure, ensuring it can support advanced analytics and AI applications while maintaining high security standards.

MyDataWork POV — The FDA's move to build a data foundation on Databricks is a strategic step for agencies with complex data needs. For those navigating regulatory environments, this offers a blueprint for balancing innovation with compliance. However, if your organization isn't bound by such stringent requirements, this might be more of an academic interest than a practical guide. The real takeaway is how government entities can leverage cutting-edge platforms without compromising on security, a lesson in marrying tradition with technology.
Discussion happens on Reddit — no comments are hosted here.
AnalysisGovernance, catalog & semantic layerDatabricks
Databricks Blog · Read source ↗

Genie Ontology aims to enhance data stacks by integrating semantic models with shared business contexts. This approach is designed to help AI agents understand and operate within the specific frameworks of different enterprises, moving beyond mere data organization to create a more cohesive operational environment.

MyDataWork POV — Genie Ontology's focus on embedding shared business context into data stacks is a refreshing shift from the usual semantic model chatter. By aligning AI agents with the nuanced realities of business operations, it promises a more coherent and actionable data environment. It's about weaving AI into the fabric of everyday business decisions, rather than merely layering more tech. The real win here is the potential for AI to become a true partner in decision-making, rather than merely a tool for data analysis.
Discussion happens on Reddit — no comments are hosted here.
CommunityData engineering & the warehouse/lakehouseAzure Data FactoryDatabricks
r/dataengineering · Read source ↗

A consulting company is working with a client to modernize their data environment. The client currently extracts data from on-premises databases like Oracle, SQL Server, and Postgres using Azure Data Factory (ADF) to transfer it into Azure Data Lake Storage (ADLS), followed by processing with Databricks.

MyDataWork POV — The notion of 'modernizing' by simply moving from on-prem to cloud raises more questions than it answers. What's missing is clarity on how this transition tackles data governance, latency, or even cost-effectiveness. Azure Data Factory and Databricks are potent tools, but are they the right fit for every legacy system being uprooted? Without dissecting the specific constraints and ambitions of the current workflow, this 'cloud-first' mantra feels like a glossy overcoat on a yet-to-be-proven structure.
Discussion happens on Reddit — no comments are hosted here.
Dataiku 1 story
Related on Reddit: search "Dataiku" ↗
Product launchGovernance, catalog & semantic layerDataiku
HPCwire · Read source ↗

Dataiku has introduced an open-source privacy layer designed to protect sensitive data, particularly in the context of generative AI advancements. This initiative aims to address data privacy concerns by providing a tool that can be integrated into existing systems to safeguard information.

MyDataWork POV — Dataiku's open-source privacy layer sounds like a step forward, but let's not get ahead of ourselves. The claim of safeguarding sensitive data in the age of generative AI is ambitious, yet the specifics of how this layer integrates with diverse data environments remain vague. Open-source is a double-edged sword; while it promises transparency, it also relies heavily on community support and expertise. Until we see real-world implementations and results, this remains more of a theoretical safeguard than a proven solution.
Discussion happens on Reddit — no comments are hosted here.
Eviden 1 story
Related on Reddit: search "Eviden" ↗
ResearchAI platforms — data science & MLEviden
Security Informed · Read source ↗

Eviden's Vision AI Solution has been included in Gartner's 2026 Matrix, highlighting its role in security-informed AI applications.

MyDataWork POV — Eviden's inclusion in the Gartner 2026 Matrix should provoke concern as well as praise. The Vision AI Solution's security-informed angle is important, but it risks overshadowing the broader implications of such AI deployments. As enterprises flock to adopt AI, there's a real danger of security becoming a checkbox rather than a foundational element. Without rigorous scrutiny, these solutions might prioritize speed over safety, leaving organizations vulnerable to unforeseen threats. Vigilance, alongside innovation, should be the guiding principle here.
Discussion happens on Reddit — no comments are hosted here.
Exa Labs 1 story
Related on Reddit: search "Exa Labs" ↗
Case studyAgentic AI, MCP & the context layerBasisClayExa Labs
OpenAI Blog · Read source ↗

Basis, Clay, and Exa Labs are leveraging AI agents to enhance workflows in areas like onboarding, account management, and developer integrations. These companies aim to demonstrate how AI-native operations can be applied within enterprises.

MyDataWork POV — The appeal of AI-native workflows is clear, yet there's a significant risk in depending too heavily on AI agents for essential tasks like onboarding and account management. These tasks demand a nuanced understanding of human interaction and context, which AI often struggles to replicate. The risk is that organizations might sacrifice depth and personalization for efficiency, leading to a hollowing out of essential human elements in customer relationships.
See how MyDataWork relates to this item
Agent Studio users should be wary of assuming that AI agents can fully replace human judgment in workflows. Instead, they can use Agent Studio to carefully scope where AI can genuinely add value without undermining the human touch, ensuring a balanced approach. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Fivetran 2 stories
Related on Reddit: search "Fivetran" ↗
AnalysisData engineering & the warehouse/lakehouseFivetran
Fivetran Blog · Read source ↗

Fivetran's blog explores how AI has revitalized performance engineering, challenging the assumption that the field had reached its limits. The piece highlights AI's role in uncovering new efficiencies and optimizing processes that were previously thought to be fully tapped.

MyDataWork POV — The influence of advanced technology on performance engineering transcends a mere technical upgrade; it represents a fundamental transformation. Fivetran's insight that AI discovered unexpected efficiencies highlights its significant potential. But the real kicker? The goal is to redefine what we thought the lemon was capable of, rather than merely squeezing more juice from it. This is a reimagining of performance engineering's potential, transforming what seemed like a plateau into a new frontier.
Discussion happens on Reddit — no comments are hosted here.
AnalysisData engineering & the warehouse/lakehouseFivetran
Fivetran Blog · Read source ↗

Fivetran's blog outlines six ways that Open Data Infrastructure can streamline workflows by reducing inefficiencies. The focus is on how open data systems can enhance data accessibility and integration, thereby improving overall workflow efficiency.

MyDataWork POV — Open Data Infrastructure is a boon for those drowning in siloed data and cumbersome integrations. If your workflows are bogged down by constant data wrangling, this approach offers a lifeline. By simplifying access and reducing friction, it empowers data teams to focus on analysis rather than logistics. However, if your current setup already runs smoothly, the benefits might not justify the overhaul. This is a targeted solution for teams battling data chaos, not a universal upgrade for all.
Discussion happens on Reddit — no comments are hosted here.
Google Cloud 3 stories
Related on Reddit: search "Google Cloud" ↗
Product launchAI platforms — data science & MLGoogle Cloud
Google Cloud — Data & AI · Read source ↗

Google's BigQuery introduces TabFM, a new approach to predictive analytics that aims to simplify and accelerate the process of building models for tasks like churn prediction and fraud scoring. Traditionally reliant on complex cycles involving XGBoost, Random Forest, or DNNs, this new tool promises to reduce the manual overhead associated with feature engineering and hyperparameter tuning.

MyDataWork POV — TabFM in BigQuery is a refreshing shift for predictive analytics. By streamlining the cumbersome train-tune-deploy-retrain cycle, it offers a more accessible route to insights without the usual manual drudgery. This is a practical boon for teams that have been bogged down by the complexities of traditional model-building, alongside a technical upgrade. It highlights a significant shift: the future of enterprise AI focuses on integrating nimble, efficient tools that make data work more transparent and manageable.
Discussion happens on Reddit — no comments are hosted here.
Product launchData engineering & the warehouse/lakehouseGoogle Cloud
Google Cloud — Data & AI · Read source ↗

BigQuery Graph has reached general availability, offering a solution to enterprise data challenges by focusing on connections rather than individual data points. This tool addresses complex queries about relationships between data entities, which traditionally required separate graph databases, leading to data silos.

MyDataWork POV — BigQuery Graph's arrival is a significant milestone for data professionals. By integrating graph capabilities directly into BigQuery, it eliminates the need for separate graph databases and the silos they create. Efficiency enables more sophisticated analyses directly where your data lives. The real breakthrough here is the potential for richer, more contextual insights without the operational overhead of moving data around. For data teams, this means less time wrestling with infrastructure and more time extracting actionable insights.
Discussion happens on Reddit — no comments are hosted here.
Product launchData engineering & the warehouse/lakehouseGoogle Cloud
Google Cloud — Data & AI · Read source ↗

Google Cloud NEXT '26 introduced the Orchestration Pipelines framework, aiming to reduce the complexity and time required to set up data pipelines from weeks to minutes. This initiative seeks to make pipeline orchestration more accessible to data professionals who previously faced barriers to entry.

MyDataWork POV — The promise of reducing pipeline setup from weeks to mere minutes sounds enticing, but it raises an important concern: the potential for oversimplification. By streamlining processes to this degree, there's a risk of bypassing critical checks and balances that ensure data integrity and security. In our effort to democratize access, we might inadvertently compromise the foundation that supports reliable data-driven decisions. This focuses on speed while also maintaining the rigor that underpins trustworthy analytics.
Discussion happens on Reddit — no comments are hosted here.
Ingent 1 story
Related on Reddit: search "Ingent" ↗
Vendor newsData engineering & the warehouse/lakehouseIngent
finance.biggo.com · Read source ↗

Ingent has rejoined the Gartner briefing after a four-year hiatus, setting its sights on the commercial database migration market with its new offering, XperDB. This move marks Ingent's renewed effort to penetrate a competitive space dominated by established players.

MyDataWork POV — Ingent's reentry with XperDB into the database migration market raises more questions than it answers. After a four-year absence, the company faces an uphill battle to prove its tool can compete against entrenched giants like AWS and Microsoft. The claim of targeting commercial DB migration is bold, but what's conspicuously absent is any demonstration of how XperDB plans to distinguish itself in terms of performance or cost-efficiency. Without clear differentiators, this could be a return that fizzles more than it flames.
Discussion happens on Reddit — no comments are hosted here.
Microsoft 1 story
Related on Reddit: r/PowerBI · search "Microsoft" ↗
Product launchData engineering & the warehouse/lakehouseMicrosoft
Nextgov/FCW · Read source ↗

Microsoft has launched its Fabric integration platform specifically for select federal customers. This move aims to enhance data integration and management capabilities within federal agencies, offering a tailored solution to meet their unique security and operational needs.

MyDataWork POV — Microsoft's decision to roll out Fabric for select federal customers is a strategic masterstroke. By targeting federal agencies, Microsoft is positioning itself as a key player in secure data management. The federal focus means Fabric will likely prioritize compliance and security features, which could set a new standard for how integration platforms operate in sensitive environments. This focuses on functionality, trust, and reliability in high-stakes contexts.
Discussion happens on Reddit — no comments are hosted here.
OpenAI 2 stories
Related on Reddit: r/OpenAI · search "OpenAI" ↗
Case studyGovernance, catalog & semantic layerOpenAI
OpenAI Blog · Read source ↗

Gilbert + Tobin, a law firm, is leveraging CEO leadership, strict governance, and human accountability to implement and scale ChatGPT Enterprise and Codex within their operations.

MyDataWork POV — Scaling AI like ChatGPT Enterprise and Codex at Gilbert + Tobin sounds methodical, but how much does 'CEO-led commitment' really anchor the process? The claim of rigorous governance is compelling, yet it sidesteps the tangible metrics that would demonstrate its effectiveness. Without clear evidence of how these systems tangibly enhance legal work, it's hard to see past the buzzwords. The spotlight on 'human accountability' feels more like a safety net than a strategy.
Discussion happens on Reddit — no comments are hosted here.
Case studyAI platforms — data science & MLOpenAI
OpenAI Blog · Read source ↗

Polimill is developing a new AI infrastructure for Japan, utilizing OpenAI's GPT models and Codex. The initiative aims to enhance how municipalities access and leverage administrative knowledge, speeding up development processes.

MyDataWork POV — Polimill's approach to Japan's public AI infrastructure is a smart move, leveraging OpenAI's GPT models and Codex to streamline municipal operations. By concentrating on administrative knowledge, Polimill embeds AI into the daily operations of governance rather than merely applying technology to a problem. This focuses on establishing a strong foundation for AI-driven municipal efficiency. The real win here is the potential for municipalities to operate with the agility and insight that AI promises, making government processes less opaque and more responsive.
Discussion happens on Reddit — no comments are hosted here.
Power BI 1 story
Related on Reddit: r/PowerBI · search "Power BI" ↗
NewsBI & analytics toolsPower BI
TechRepublic · Read source ↗

The article provides a step-by-step guide on creating a hierarchy in Microsoft Power BI to enable drill mode, allowing users to navigate through different levels of data granularity.

MyDataWork POV — Power BI's drill mode hierarchy might seem like a straightforward enhancement, but it risks oversimplifying complex data relationships. By focusing on hierarchical navigation, there's a danger of reinforcing linear thinking in data analysis, potentially obscuring more nuanced insights. Users may become too reliant on predefined paths, missing out on the rich, interconnected nature of real-world data. This approach could inadvertently narrow the analytical lens, leading to decisions based on incomplete pictures.
Discussion happens on Reddit — no comments are hosted here.
RapidMiner 1 story
Related on Reddit: search "RapidMiner" ↗
AnalysisAI platforms — data science & MLRapidMinerAlteryx
TechRepublic · Read source ↗

TechRepublic compares RapidMiner and Alteryx, two prominent data science platforms, evaluating their features, usability, and performance. The article aims to help organizations decide which software better fits their data analysis needs.

MyDataWork POV — The head-to-head comparison of RapidMiner and Alteryx reveals a deeper concern: the growing complexity of data science tools that can overwhelm teams. While both platforms offer powerful capabilities, the risk lies in their steep learning curves and the potential for underutilization. Without adequate training and strategic alignment, organizations may find themselves with sophisticated tools that remain largely untapped, leading to wasted resources and missed opportunities.
Discussion happens on Reddit — no comments are hosted here.
Redpanda 1 story
Related on Reddit: search "Redpanda" ↗
AnalysisData engineering & the warehouse/lakehouseConfluentAWSRedpanda
tech-insider.org · Read source ↗

The comparison between Confluent Cloud, AWS MSK, and Redpanda Cloud in 2026 highlights their respective strengths and weaknesses in cloud-based data streaming. Confluent Cloud is known for its robust ecosystem and integration capabilities, AWS MSK emphasizes seamless AWS service integration, while Redpanda Cloud offers unique efficiency and low-latency advantages.

MyDataWork POV — If you're juggling massive streaming data pipelines, Confluent Cloud's rich ecosystem could be your ally, offering integration ease that might just simplify your life. But if your world revolves around AWS, you'll appreciate AWS MSK for its seamless service tie-ins, though be ready to navigate its complexity. Redpanda Cloud, however, is the dark horse with its efficiency and low-latency perks—ideal for those ready to step outside giant shadows for performance gains. It's not about picking a winner; it's about finding your fit.
Discussion happens on Reddit — no comments are hosted here.
Roche 1 story
Related on Reddit: search "Roche" ↗
Case studyAI platforms — data science & MLRoche
ThoughtSpot Blog · Read source ↗

At the Agentic Analytics Playbook event in London, Yannick Misteli from Roche highlighted a key reason AI pilots often fail: the lack of addressing 'day after' questions. These are the practical considerations that arise once a pilot is operational, beyond initial technology or budget concerns.

MyDataWork POV — Roche's insight into AI pilot failures is a wake-up call for data teams. It's not the tech or the budget that trips us up—it's the mundane, post-launch realities. Misteli's focus on 'day after' questions is a reminder that operationalizing AI requires more than a flashy pilot. It's about preparing for the everyday grind of maintenance and iteration. This is a message for those ready to move beyond the pilot phase, not for those still dazzled by initial AI promises.
See how MyDataWork relates to this item
Agent Studio can help teams like Roche's by providing a structured way to scope and define agentic use cases before they hit the 'day after' phase. By using the five-step flow to plan and document, teams can better anticipate and address post-launch challenges. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
SAP 3 stories
Related on Reddit: search "SAP" ↗
AnalysisAI platforms — data science & MLSAP
SAP News (Data & Analytics) · Read source ↗

The article discusses strategies to optimize AI for both individual users and entire organizations, emphasizing the need to bridge the gap between personal and institutional value. It suggests that AI should enhance not just personal productivity but also contribute to broader organizational goals.

MyDataWork POV — AI's potential to bridge individual and institutional value is a thrilling proposition for data professionals. When AI tools are designed to serve both personal productivity and organizational goals, they become more than just isolated assets; they become the connective tissue binding disparate efforts into a coherent whole. This dual focus ensures that the benefits of AI extend beyond the individual, fostering a collaborative environment where data work is visible and valued. It's this expansive vision, rather than a single-vendor promise, that will truly empower enterprise AI.
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ResearchData engineering & the warehouse/lakehouseSAP
SAP News (Data & Analytics) · Read source ↗

The IDC Business Value White Paper reports that customers using the SAP Integration Suite experience a 368% return on investment and achieve payback in just eight months. This suggests significant benefits across various integration points within modern enterprises.

MyDataWork POV — SAP Integration Suite's reported 368% ROI and eight-month payback highlight a significant change in how enterprises can leverage integration. This focuses on establishing a seamless flow that enhances every aspect of the business. When integration becomes this effective, it's not merely a technical upgrade—it's a strategic advantage that turns data into actionable insights, driving real business value.
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Case studyData engineering & the warehouse/lakehouseSAP
SAP News (Data & Analytics) · Read source ↗

Wilson Sporting Goods has enhanced its operations by leveraging SAP's cloud solutions, allowing for improved efficiency and performance. The integration aims to streamline processes and provide real-time insights.

MyDataWork POV — Wilson's embrace of SAP represents a strategic leap forward. By harnessing SAP's cloud capabilities, Wilson gains speed and agility, enabling smarter decision-making. This move illustrates how enterprise AI is a mosaic of solutions rather than a single entity. The real win here is how Wilson can now pivot with precision, transforming data into actionable insights, showcasing the power of tailored integration.
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SAS 1 story
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AnalysisAI platforms — data science & MLSAS
SAS Blogs · Read source ↗

The article discusses the use of Generative AI (GenAI) to interpret and enhance statistical models. While models can run quickly, understanding their outputs, such as fit statistics, residual plots, and decision trees, requires more time and insight. GenAI aims to bridge this gap by providing deeper insights into model performance and assumptions.

MyDataWork POV — Relying on GenAI to interpret statistical models might sound like a shortcut, but it risks oversimplifying complex analyses. The danger lies in users mistaking AI-generated summaries for comprehensive understanding. Fit statistics and residual plots are nuanced; GenAI's interpretations could gloss over critical subtleties, leading to misguided decisions. Without rigorous human oversight, the promise of 'understanding' could devolve into mere surface-level insights, leaving data professionals with a false sense of security.
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Snowflake 1 story
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Product launchData engineering & the warehouse/lakehouseSnowflake
StartupHub.ai · Read source ↗

Claude Fable 5.1, an AI platform, has been made available for private preview on Snowflake, promising to enhance data insights and analytics capabilities.

MyDataWork POV — The assertion that Claude Fable 5.1 will seamlessly enhance Snowflake's analytics capabilities is a bold claim that warrants scrutiny. The integration promises improved insights, but without evidence of practical deployments or user feedback, it remains speculative. What’s missing is a clear demonstration of how Claude’s AI will navigate the complexities of Snowflake’s existing data environments without becoming just another layer of complexity itself.
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ThoughtSpot 1 story
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Case studyBI & analytics toolsThoughtSpot
ThoughtSpot Blog · Read source ↗

PAYBACK, a major German loyalty program, has overhauled its reporting system, shifting from a cumbersome, manual process to a self-service data culture. This transformation has streamlined report adjustments, which previously required extensive manual intervention, leading to delays and user frustration.

MyDataWork POV — PAYBACK's shift to a self-service data model is a win for data professionals and users alike. By eliminating the bottleneck of manual report adjustments, analysts can now focus on more strategic tasks rather than drowning in ticket requests. This change not only speeds up data access but also empowers users to explore insights independently. Real progress in data work often arises from rethinking our interactions with data rather than focusing solely on the data itself. PAYBACK's approach highlights the importance of building systems that adapt to user needs, rather than forcing users to adapt to rigid systems.
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dbt 1 story
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AnalysisAI platforms — data science & MLdbt
dbt Labs Blog · Read source ↗

AI projects often stall not due to model issues but because of a lack of trusted context. The article suggests that solving this context gap is key to moving AI initiatives forward.

MyDataWork POV — Blaming the 'context gap' for stalled AI projects feels like a convenient scapegoat. The real issue might be deeper: a fundamental misunderstanding of the data environment. Many organizations rush into AI without a clear map of their existing workflows or data dependencies. Adding context involves understanding the intricate web of data interactions that already exist. Until teams grasp this, AI will remain a stalled promise.
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