tgroenwals shared this post · Jun 22

𝐖𝐡𝐚𝐭 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐞𝐬 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐟𝐫𝐨𝐦 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬?

It’s rarely the technology.

It’s the architecture decisions made before the first pipeline is built.

Many organizations invest heavily in cloud platforms, analytics tools, and AI initiatives-yet struggle with fragmented data, inconsistent metrics, and slow delivery.

The reason is simple:

Different business needs require different warehouse design patterns.

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 6 𝐜𝐨𝐦𝐦𝐨𝐧 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐞𝐯𝐞𝐫𝐲 𝐝𝐚𝐭𝐚 𝐥𝐞𝐚𝐝𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝:…

Vinod Bijlani Most data initiatives struggle long before any dashboard appears in front of users. The early structure decisions quietly shape every outcome that follows. Jun 22 1 like

Raghavendra Bagalkoti Architecture choices tend to stay around much longer than tool selections. Jun 22 2 likes

tgroenwals shared this post · Jun 6

Ashish Joshi

Jun 5 · archived Jun 6

Most teams think a data pipeline is just ETL.

That mindset does not survive at scale.

In 2026, data pipelines are no longer moving data from A to B.

They are powering:
→ Analytics
→ AI systems
→ Real-time decisions
→ Business operations

And every missing layer becomes a future bottleneck.

The highest-performing data platforms are built as interconnected systems, not isolated pipelines.

That means thinking beyond ingestion.

𝐀 𝐦𝐨𝐝𝐞𝐫𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬:

→ 𝐃𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧
• Batch, streaming, and CDC patterns
• Reliable data capture at scale…

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Himani Bansal Data quality and observability are often the most underestimated layers because issues there can silently impact every dashboard, model, and business decision downstream. Jun 5 2 likes

Anika Verma What makes this directly relevant for AI systems is that pipeline gaps don't surface at the pipeline layer but surface in the AI output. By the time the model returns a wrong answer, the root is often in validation or governance, not in the model itself. Jun 5 1 like

tgroenwals shared this post · Jun 3

Ashish Joshi

Jun 2 · archived Jun 3

Most companies think they have a data strategy.
What they actually have is data everywhere.

Different tools.
Different pipelines.
Different definitions of truth.

And when leadership asks for insight, the organization spends weeks reconciling numbers.

The problem is rarely analytics.
It is architecture.

Behind every modern product, AI system, and business decision sits a Big Data architecture that determines how fast the company can think.

𝐀𝐭 𝐬𝐜𝐚𝐥𝐞, 𝐭𝐡𝐞 𝐟𝐥𝐨𝐰 𝐮𝐬𝐮𝐚𝐥𝐥𝐲 𝐥𝐨𝐨𝐤𝐬 𝐥𝐢𝐤𝐞 𝐭𝐡𝐢𝐬:…

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Bhavishya Bharadwaj The real challenge often begins when multiple teams are making decisions from slightly different versions of the same information. Jun 2 1 like

Vinod Bijlani Clarity in data systems often becomes the hidden differentiator between speed and stagnation in decision cycles. Jun 2 1 like

tgroenwals shared this post · Jun 1

Ashish Joshi

Jun 1 · archived Jun 1

Most organizations think data lineage is about tracking pipelines.

That is no longer enough.

𝐈𝐧 2026, 𝐥𝐢𝐧𝐞𝐚𝐠𝐞 𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬-𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐥𝐚𝐲𝐞𝐫 𝐟𝐨𝐫:

→ AI systems
→ Executive reporting
→ Regulatory compliance
→ Decision accountability

Because when numbers change unexpectedly, one question determines trust:

“Can you explain where this came from?”

𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐬 𝐭𝐡𝐚𝐭 𝐭𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐥𝐢𝐧𝐞𝐚𝐠𝐞 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬 𝐰𝐞𝐫𝐞 𝐛𝐮𝐢𝐥𝐭 𝐟𝐨𝐫:

• Batch pipelines
• Static schemas
• Centralized systems

Modern data ecosystems no longer operate that way.

𝐓𝐨𝐝𝐚𝐲’𝐬 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:…

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Harish Agoram Ashish, spot on. Moving from passive tracking to active decision trust is the ultimate unlock. When we prioritize comprehensive lineage across all modern workflows, we transform a major operational blind spot into our greatest strategic advantage. Jun 1 2 likes

Anika Verma Lineage is useful if every step that touches the data is captured. The moment a decision happens outside the tracked system; a manual override, an Excel edit, an AI transformation without logging, the trail has gaps you don’t know about. Jun 1 1 like

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