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Vendors in the news

17 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 1Alteryx 1Amazon 1Amazon Redshift 1Databricks 3Dataiku 1Fivetran 4Google 1Google Cloud 4KINETIQ 1Looker 1Microsoft Azure 1OpenAI 1SAP 4SAS 1Scaleway 1Snowflake 1
AWS 1 story
Related on Reddit: r/aws · search "AWS" ↗
Product launchData engineering & the warehouse/lakehouseAWS
AWS Big Data Blog · Read source ↗

AWS Glue Data Quality has been integrated into Amazon EMR, specifically in the AWS runtime for Apache Spark version 8.1.0. This integration allows users to apply Data Quality Definition Language (DQDL) rulesets directly within EMR Spark jobs. The release introduces two usage patterns: inline validation and a more complex Apache Iceberg Write-Audit-Publish workflow with managed rulesets.

MyDataWork POV — Finally, AWS is making it easier for data teams to ensure quality directly within their EMR workflows. By embedding AWS Glue Data Quality into EMR, teams can now apply DQDL rulesets as they process data with Spark. This integration is a boon for practitioners tired of piecemeal solutions, as it promises consistent data integrity checks across workflows without the overhead of separate quality assurance stages. It's a practical move that acknowledges the fragmented reality of enterprise data environments, aiming for seamless stitching rather than isolated patches.
Discussion happens on Reddit — no comments are hosted here.
Alteryx 1 story
Related on Reddit: r/alteryx · search "Alteryx" ↗
Product launchData engineering & the warehouse/lakehouseAlteryxGoogle
IT Brief Asia · Read source ↗

Alteryx has introduced new BigQuery invoice workflows specifically designed for finance teams. This development aims to streamline financial processes by leveraging the capabilities of BigQuery within the Alteryx platform.

MyDataWork POV — Alteryx's launch of BigQuery invoice workflows for finance might sound like a step forward, but it's worth asking whether this is truly a solution or just another layer of complexity. The promise of streamlined processes is enticing, yet the practical implementation remains unproven. Finance teams need more than just a workflow—they need assurance that these tools will genuinely integrate with their existing systems without creating additional headaches. It's a claim that demands scrutiny before celebration.
Discussion happens on Reddit — no comments are hosted here.
Amazon 1 story
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NewsData engineering & the warehouse/lakehouseAmazon
AWS Big Data Blog · Read source ↗

Amazon's guide details how to construct cross-account streaming pipelines utilizing Kinesis Data Streams with the Kinesis Producer Library and Kinesis Client Library across multiple AWS accounts. It emphasizes the use of resource-based policies, cross-account IAM, and a shared AWS KMS key to ensure security.

MyDataWork POV — Cross-account streaming pipelines sound efficient, but the complexity and security risks are significant. Managing intricate IAM setups and resource policies across multiple accounts could introduce vulnerabilities if not meticulously configured. The promise of security isolation is only as strong as the weakest link in these configurations, leaving enterprises potentially exposed to cross-account data breaches. This intricate setup demands expertise and constant vigilance, otherwise, it risks becoming a Pandora's box of security oversights.
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Amazon Redshift 1 story
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Vendor newsData engineering & the warehouse/lakehouseAmazon Redshift
AWS Big Data Blog · Read source ↗

Amazon Redshift has introduced a feature that automatically resolves many concurrent write conflicts for Apache Iceberg tables. This update allows non-overlapping writes to be committed without manual retries, enhancing throughput and efficiency.

MyDataWork POV — This update is a refreshing step forward for data professionals dealing with the complexities of concurrent writes. By intelligently rebasing non-overlapping operations, Amazon Redshift alleviates a persistent bottleneck, allowing teams to focus more on insights and less on error resolution. It highlights a broader trend: the future of enterprise AI and data management focuses on seamlessly integrating diverse components instead of depending on a single solution. This move by Amazon Redshift exemplifies how incremental improvements in handling data operations can lead to significant gains in productivity.
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Databricks 3 stories
Related on Reddit: r/databricks · search "Databricks" ↗
Product launchData engineering & the warehouse/lakehouseDatabricks
Databricks Blog · Read source ↗

Databricks has launched a Private Network Gateway for its serverless compute offering. This new feature is fully managed, intelligent, and versionless, aiming to enhance security and simplify network management for users.

MyDataWork POV — Databricks' Private Network Gateway is a smart move for data teams looking to streamline secure access without the usual network headaches. By offering a versionless, fully managed solution, Databricks is addressing a common pain point: the complexity of maintaining network configurations. This focuses on enabling data professionals to prioritize insights over infrastructure. In a world where data work often lacks a stable operational framework, this kind of simplification is a welcome relief.
Discussion happens on Reddit — no comments are hosted here.
AnalysisData engineering & the warehouse/lakehouseDatabricks
Databricks Blog · Read source ↗

The guide outlines advancements in real-time analytics projected for 2026, emphasizing the integration of live data into business decision-making processes. It discusses the evolution and potential challenges of deploying real-time systems in varied organizational contexts.

MyDataWork POV — The 2026 guide to real-time analytics offers a much-needed roadmap for organizations already drowning in data influx. It's a wake-up call for companies still operating on outdated batch processing systems. Yet, the guide perhaps underestimates the cultural shift required within teams to embrace real-time thinking. Not every business is ready for the immediacy this entails, and many may find themselves unprepared for the velocity of decision-making it demands. Leaders should gauge if their teams are truly ready for this leap.
See how MyDataWork relates to this item
MyDataWork's ability to catalog data within the warehouse without waiting for specific connectors ensures compatibility with real-time analytics solutions as they evolve, providing seamless integration with current and future tools. Explore MyDataWork ↗
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Case studyAI platforms — data science & MLDatabricks
Databricks Blog · Read source ↗

Flo Health has partnered with Databricks AI Runtime to develop generative AI models that prioritize safety and utility. The company, which serves 81 million monthly users, aims to enhance their AI capabilities while maintaining a high standard for data protection and user trust.

MyDataWork POV — Flo Health’s commitment to safety with Databricks AI Runtime sounds admirable, but the scale introduces a glaring vulnerability: data privacy. With 81 million monthly users, any breach or mishandling could have catastrophic consequences. The pressure to innovate quickly might tempt shortcuts in data governance protocols. We cannot assume that just because a model is 'safer,' it's airtight. The sheer volume of sensitive data demands an unyielding commitment to transparency and continuous oversight, lest we equate speed with security.
Discussion happens on Reddit — no comments are hosted here.
Dataiku 1 story
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Vendor newsAI platforms — data science & MLScalewayDataiku
HPCwire · Read source ↗

Scaleway and Dataiku have announced a partnership aimed at enhancing AI capabilities for businesses across Europe. The collaboration focuses on integrating Scaleway's cloud infrastructure with Dataiku's AI platform to streamline the development and deployment of AI solutions.

MyDataWork POV — The Scaleway and Dataiku partnership is a smart move for European businesses looking to harness AI without getting bogged down in vendor lock-in. Scaleway's strong cloud infrastructure combined with Dataiku's flexible AI platform provides a modular approach that acknowledges the complexity of real-world data work. This collaboration emphasizes the need for adaptable, interoperable systems in developing AI capabilities, enabling enterprises to create AI solutions tailored to their specific requirements instead of relying on a uniform approach.
Discussion happens on Reddit — no comments are hosted here.
Fivetran 4 stories
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NewsData engineering & the warehouse/lakehouseFivetran
Fivetran Blog · Read source ↗

Fivetran has introduced AI connectors designed to centralize AI usage data, helping organizations track spending, user engagement, and productivity improvements. These connectors aim to consolidate fragmented AI consumption data into actionable insights.

MyDataWork POV — Fivetran’s AI connectors are a boon for organizations drowning in disparate AI usage data. By centralizing this information, they offer clarity on spending and productivity metrics. However, if your team isn't already grappling with scattered AI data or lacks a mature AI strategy, this tool might be overkill. It's a targeted solution for those already deep in AI adoption, not a starting point for beginners. The value lies in its ability to streamline existing complexity, not create new pathways.
Discussion happens on Reddit — no comments are hosted here.
Product launchData engineering & the warehouse/lakehouseFivetran
Fivetran Blog · Read source ↗

Fivetran has introduced a Managed Data Lake Service aimed at streamlining the maintenance of data lake tables. This service promises to deliver data that is optimized for queries and consistently synchronized, reducing the complexity for users.

MyDataWork POV — Fivetran's Managed Data Lake Service is important because it simplifies the complex task of maintaining data lake tables. By ensuring data is always query-ready and in sync, Fivetran tackles a significant challenge for data teams. However, the real intrigue is in how this service might shift the balance of power from IT departments to data analysts, enabling them to focus more on insights rather than infrastructure. This could democratize data access in a meaningful way, fostering a culture of self-service analytics.
Discussion happens on Reddit — no comments are hosted here.
Vendor newsData engineering & the warehouse/lakehouseFivetran
Fivetran Blog · Read source ↗

Fivetran has announced a reduction in pricing alongside new product enhancements aimed at improving the scalability of their data pipeline solutions. The changes are designed to provide more value to customers as they scale their data operations.

MyDataWork POV — The decision to cut prices while enhancing pipeline capabilities is a strategic maneuver aimed at attracting growth-oriented data teams. For large enterprises managing vast datasets, this simplification and cost efficiency can be a godsend, enabling seamless data flow at scale. However, smaller teams, who don't operate at such scale, may find these enhancements less impactful. They're better served focusing on tools that address their unique, immediate needs without the overhead of enterprise-level complexities.
Discussion happens on Reddit — no comments are hosted here.
NewsData engineering & the warehouse/lakehouseFivetran
Fivetran Blog · Read source ↗

Fivetran has introduced a Managed Data Lake Service (MDLS) aimed at simplifying lakehouse governance. The service emphasizes observability, access control, and data lifecycle management to streamline data management processes.

MyDataWork POV — Fivetran's Managed Data Lake Service hits a sweet spot for mid-sized enterprises juggling complex data environments. By focusing on observability and access control, it promises to cut through the chaos of lakehouse management. However, smaller outfits entrenched in straightforward data flows might find it a solution in search of a problem. The real win here is for those who already feel the pain of sprawling data estates and need a hand in taming them, not those who manage a neat data pond.
Discussion happens on Reddit — no comments are hosted here.
Google 1 story
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Product launchData engineering & the warehouse/lakehouseAlteryxGoogle
IT Brief Asia · Read source ↗

Alteryx has introduced new BigQuery invoice workflows specifically designed for finance teams. This development aims to streamline financial processes by leveraging the capabilities of BigQuery within the Alteryx platform.

MyDataWork POV — Alteryx's launch of BigQuery invoice workflows for finance might sound like a step forward, but it's worth asking whether this is truly a solution or just another layer of complexity. The promise of streamlined processes is enticing, yet the practical implementation remains unproven. Finance teams need more than just a workflow—they need assurance that these tools will genuinely integrate with their existing systems without creating additional headaches. It's a claim that demands scrutiny before celebration.
Discussion happens on Reddit — no comments are hosted here.
Google Cloud 4 stories
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AnalysisData engineering & the warehouse/lakehouseGoogle Cloud
Google Cloud — Data & AI · Read source ↗

Organizations often find their data dispersed across multiple clouds and operational systems. To support analytical and AI initiatives, they face the challenge of integrating data from these diverse sources, traditionally requiring complex extraction processes to centralize information into a single repository.

MyDataWork POV — The idea of 'cross-cloud conversational analytics' is enticing, yet the dependence on 'open standards' as a cure-all raises concerns. It's a seductive narrative, yet one that glosses over the gritty, unresolved tension between diverse cloud environments and true interoperability. While open standards promise simplicity, they rarely account for the nuanced intricacies of data provenance and governance across disparate systems. Until these foundational issues are addressed, the promise remains tenuous.
See how MyDataWork relates to this item
MyDataWork users can leverage direct connections to cloud platforms without installation, simplifying access to dispersed data across warehouses like Snowflake and analytics tools like Tableau, potentially easing some cross-cloud integration challenges. Explore MyDataWork ↗
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Product launchBI & analytics toolsGoogle CloudLooker
Google Cloud — Data & AI · Read source ↗

Looker introduces Trusted Data Apps for the Agentic Data Cloud, aiming to enhance the business intelligence experience. The focus is on moving beyond static dashboards and CSV exports to create more dynamic and interactive data presentations.

MyDataWork POV — Looker's push for Trusted Data Apps in the Agentic Data Cloud is a breath of fresh air for BI users tired of static dashboards. By enabling more interactive and tailored data experiences, Looker addresses a persistent pain point: the disconnect between data presentation and actionable insights. This approach acknowledges that enterprise AI thrives on interconnected systems and highlights the need for data tools to evolve beyond mere data access to offer richer, more engaging user experiences.
See how MyDataWork relates to this item
MyDataWork users can leverage direct connectors to tools like Looker to gain visibility into the source-table lineage of dashboards and datasets. This helps ensure that changes in data sources are tracked and managed effectively, preventing disruptions in data-driven decision-making. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Case studyAI platforms — data science & MLGoogle Cloud
Google Cloud — Data & AI · Read source ↗

The Data Agent Kit offers a streamlined approach to fraud detection by enabling data scientists to ingest, clean, and analyze transaction logs. It facilitates the training of models on historical fraud data and automates the scoring of new records, with the capability to run these processes daily.

MyDataWork POV — The Data Agent Kit's importance stems from its capacity to streamline fraud detection workflows effectively. By automating the ingestion and analysis of transaction logs, it reduces the manual burden on data scientists, allowing them to focus on interpreting results rather than wrestling with data prep. Catching fraud faster frees up analytical capacity for deeper insights. The kit's daily scheduling feature ensures that the model remains relevant, adapting to new patterns as they emerge.
Discussion happens on Reddit — no comments are hosted here.
Product launchData engineering & the warehouse/lakehouseGoogle Cloud
Google Cloud — Data & AI · Read source ↗

TimesFM 3.0 introduces multivariate time-series forecasting capabilities in BigQuery. This upgrade allows users to generate forecasts directly in SQL, expanding beyond the previous univariate limitations of TimesFM 2.5.

MyDataWork POV — The leap to multivariate forecasting in TimesFM 3.0 might seem like progress, but it highlights concerns about managing complexity. While SQL-based forecasting simplifies model handling, the intricacies of multivariate data can easily lead to misinterpretation. Without a solid framework for understanding variable interactions, users risk producing forecasts that mislead rather than inform. The promise of ease could mask the peril of oversimplification, leaving data teams vulnerable to flawed insights.
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KINETIQ 1 story
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Vendor newsData engineering & the warehouse/lakehouseKINETIQ
The National Law Review · Read source ↗

KINETIQ has been recognized as a featured partner for Microsoft Fabric, highlighting its role in enhancing data integration and analytics capabilities. This partnership aims to leverage Microsoft Fabric's tools to streamline data processes for enterprises.

MyDataWork POV — KINETIQ's recognition as a Microsoft Fabric Featured Partner is a promising development for enterprises seeking cohesive data solutions. By aligning with Microsoft Fabric, KINETIQ positions itself to offer more seamless data integration and analytics processes. This partnership highlights the necessity of interlocking solutions in enterprise AI, where real value comes from integrating diverse tools into a coherent workflow. It's a step toward creating a more comprehensive and accessible data ecosystem, which is essential for organizations aiming to harness their data's full potential.
Discussion happens on Reddit — no comments are hosted here.
Looker 1 story
Related on Reddit: search "Looker" ↗
Product launchBI & analytics toolsGoogle CloudLooker
Google Cloud — Data & AI · Read source ↗

Looker introduces Trusted Data Apps for the Agentic Data Cloud, aiming to enhance the business intelligence experience. The focus is on moving beyond static dashboards and CSV exports to create more dynamic and interactive data presentations.

MyDataWork POV — Looker's push for Trusted Data Apps in the Agentic Data Cloud is a breath of fresh air for BI users tired of static dashboards. By enabling more interactive and tailored data experiences, Looker addresses a persistent pain point: the disconnect between data presentation and actionable insights. This approach acknowledges that enterprise AI thrives on interconnected systems and highlights the need for data tools to evolve beyond mere data access to offer richer, more engaging user experiences.
See how MyDataWork relates to this item
MyDataWork users can leverage direct connectors to tools like Looker to gain visibility into the source-table lineage of dashboards and datasets. This helps ensure that changes in data sources are tracked and managed effectively, preventing disruptions in data-driven decision-making. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Microsoft Azure 1 story
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Vendor newsData engineering & the warehouse/lakehouseMicrosoft Azure
Microsoft Azure · Read source ↗

Microsoft Azure is pushing forward hardware security by integrating open-source Hardware Security Module (HSM) technology. This move aims to enhance data protection mechanisms by allowing more customizable and transparent security solutions within their cloud infrastructure.

MyDataWork POV — Open-source HSM technology might seem like a boon for transparency, but it brings a significant risk: the potential for security vulnerabilities in the codebase. While open source allows for community review, it also opens the door to malicious actors exploiting weaknesses before patches can be applied. This could lead to a false sense of security for enterprises relying on these systems to safeguard critical data.
Discussion happens on Reddit — no comments are hosted here.
OpenAI 1 story
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Product launchData engineering & the warehouse/lakehouseOpenAI
OpenAI Blog · Read source ↗

Higgsfield's new offering extends AI-generated video capabilities, allowing creators to produce videos over 10 minutes with the enhancement of GPT-6 Astra for content, GPT-Image-2.5 for visuals, and GPT-Live for voice filmmaking. This development promises a more comprehensive toolset for creators seeking to leverage AI in video production.

MyDataWork POV — Higgsfield's promise of extending AI video lengths from one to ten minutes raises more questions than it answers. While longer videos might seem impressive, the real scrutiny lies in the quality of these outputs. Can GPT-Image-2.5 maintain visual coherence over extended narratives? And does GPT-Live genuinely deliver on voice filmmaking, or is it another half-baked feature that sounds better in the brochure than in practice? The leap in duration is bold, but without evidence of consistent quality, 'longer' might just mean 'more to fix.'
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SAP 4 stories
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AnalysisAI platforms — data science & MLSAP
SAP News (Data & Analytics) · Read source ↗

The article discusses how solving complex industry-specific problems with AI requires a deep understanding of the context, forward-deployed engineering expertise, and a focus on delivering measurable value.

MyDataWork POV — The belief that AI can address intricate industry challenges simply by implementing forward-engineered solutions is fraught with pitfalls. The real risk lies in oversimplifying the contextual nuances that differentiate one industry challenge from another. Without a nuanced understanding, AI risks becoming a blunt tool rather than a precision instrument, potentially leading to misguided strategies and wasted resources. The promise of measurable value remains elusive without this critical layer of contextual expertise.
Discussion happens on Reddit — no comments are hosted here.
Vendor newsBI & analytics toolsSAP
SAP News (Data & Analytics) · Read source ↗

SAP has introduced new features to enhance finance operations, including embedded payments, improved consolidation capabilities, and expanded AI functionalities. These advancements aim to help finance teams transition smoothly from insights to execution while ensuring governance and control.

MyDataWork POV — SAP's promise of seamless transitions from insight to execution sounds appealing, but the real-world application remains unproven. Embedding payments and expanding AI capabilities are ambitious steps, yet the actual impact on governance and control is unclear. Finance operations are notoriously complex, and without concrete examples of successful implementation, these claims risk being more aspirational than grounded in reality. The gap between announcement and execution can often be wide, and SAP needs to demonstrate how these features tangibly benefit finance teams.
See how MyDataWork relates to this item
MyDataWork users could leverage the 'Identify automation candidates' mode in the Leverage tab to evaluate SAP's new features, assessing which processes might benefit from automation and how these enhancements align with their existing workflows. Explore MyDataWork ↗
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Product launchData engineering & the warehouse/lakehouseSAP
SAP News (Data & Analytics) · Read source ↗

SAP has announced enhancements to its Cloud ERP offering, focusing on enabling organizations to transition from AI experimentation to intelligent, governed business execution. The update, known as SAP Cloud ERP Private 2025 FPS02, aims to solidify the foundation for autonomous enterprise operations.

MyDataWork POV — The latest Cloud ERP update from SAP marks significant progress for companies eager to transition from AI prototypes to practical implementations. However, it's primarily relevant for large enterprises with the resources to leverage these capabilities effectively. Smaller businesses might find the complexity and scale unnecessary for their needs. The focus on governance and intelligent execution is promising, but without clear pathways for mid-sized organizations, its impact could be limited to those already deep in the SAP ecosystem.
Discussion happens on Reddit — no comments are hosted here.
Vendor newsDecision intelligenceSAP
SAP News (Data & Analytics) · Read source ↗

SAP is enhancing its spend management solutions, including Ariba, Fieldglass, and Concur, with AI-driven capabilities. These advancements aim to help businesses make quicker and more informed spending decisions by leveraging insights from various partner innovations.

MyDataWork POV — The integration of AI across Ariba, Fieldglass, and Concur by SAP signifies a strategic pivot that reshapes spend management. By embedding AI, SAP is tackling the demand for speed and precision in financial decisions. The importance lies in how these tools can now convert raw spend data into actionable insights, potentially redefining procurement and expense management. However, the challenge will be making these insights accessible and actionable for end users, including those beyond data scientists.
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SAS 1 story
Related on Reddit: r/sas · search "SAS" ↗
Case studyAI platforms — data science & MLSAS
SAS Blogs · Read source ↗

SAS demonstrates the use of a digital twin in healthcare operations, leveraging Unreal Engine to enhance decision-making and AI model development. This initiative aims to advance operational efficiency and innovation within healthcare systems.

MyDataWork POV — This healthcare digital twin initiative using Unreal Engine deserves our attention. By simulating complex hospital environments, it provides a sandbox for operational decisions and AI innovations. However, it's the bridging of simulation and real-world application that's noteworthy. Unreal Engine, traditionally a gaming platform, may reshape how we visualize healthcare challenges, offering a fresh way to model outcomes. It’s less about digital replicas and more about the potential for proactive, data-driven decision-making in high-stakes environments.
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Scaleway 1 story
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Vendor newsAI platforms — data science & MLScalewayDataiku
HPCwire · Read source ↗

Scaleway and Dataiku have announced a partnership aimed at enhancing AI capabilities for businesses across Europe. The collaboration focuses on integrating Scaleway's cloud infrastructure with Dataiku's AI platform to streamline the development and deployment of AI solutions.

MyDataWork POV — The Scaleway and Dataiku partnership is a smart move for European businesses looking to harness AI without getting bogged down in vendor lock-in. Scaleway's strong cloud infrastructure combined with Dataiku's flexible AI platform provides a modular approach that acknowledges the complexity of real-world data work. This collaboration emphasizes the need for adaptable, interoperable systems in developing AI capabilities, enabling enterprises to create AI solutions tailored to their specific requirements instead of relying on a uniform approach.
Discussion happens on Reddit — no comments are hosted here.
Snowflake 1 story
Related on Reddit: r/snowflake · search "Snowflake" ↗
Case studyData engineering & the warehouse/lakehouseSnowflake
Snowflake · Read source ↗

Aviv has integrated 25 years of legacy data into a unified platform, aiming to revolutionize digital real estate by leveraging Snowflake's cloud capabilities.

MyDataWork POV — Integrating 25 years of legacy data sounds like a monumental task, but the claim that it will 'shape the future of digital real estate' needs scrutiny. Without clear insights into how Aviv intends to tackle data quality issues inherent in such a vast, heterogeneous dataset, the unified platform risks becoming a glossy veneer over a complex mess. The specifics of their data cleansing and contextualization processes remain unproven.
Discussion happens on Reddit — no comments are hosted here.
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