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The Future of Data Management: Understanding the Snowflake and Databricks Acquisitions

· AI

Recently, I've found myself in the company of various C-Suiteexecutives, including CEOs, CHROs, and CIOs. The talk of the town? The tech world's recent acquisitions – Snowflake's acquisition of Neeva and Databricks' acquisition of MosaicML. It's causing quite a stir, and these leaders are now
evaluating their allegiances to these platforms, wondering whether they should stay the course or make a switch. The question isn't idle chatter. It's a strategic consideration that could shape the future of their respective companies.

As these discussions unfolded, it became clear thatunderstanding these acquisitions and what they mean for businesses was crucial. So, I took the liberty of discussing these developments with the wider community of data and tech enthusiasts on LinkedIn. From these conversations,
I've derived a simple "rule of thumb" to guide these C-Suite executives in their decision-making.

Snowflake,with its acquisition of Neeva, is now a powerhouse in running inference at large scale. The significance? In the world of machine learning, 80-90% of costs are typically tied to inference – making predictions using machine learning models. If your organization heavily relies on making predictions from
models, such as recommendation systems or predictive analytics, you might want to consider sticking with or moving to Snowflake. They're stepping up their game.

Meanwhile,Databricks' acquisition of MosaicML has streamlined the training of large
language models (LLMs). If your organization has a keen interest in creating and training its own models, Databricks, armed with their new capabilities, might be the better choice for you.

But let's dive a little deeper.

Use Cases:

Snowflake is focusing on indexing enterprise data (think of it as
organizing a vast amount of data in a searchable way) and facilitating quick
and cost-effective inference, particularly for search-related applications.
Databricks is enabling businesses to create their own large language models
from their data, likely for applications like search or chatbots.

Rule of Thumb: If your business has large amounts of data that you want to make more
searchable or usable, Snowflake might be the right choice. If you're more
interested in developing custom language understanding applications (like chatbots),
look into Databricks.

Quality of Data:

Both companies may be underestimating the quality of enterprise data,
which is often unstructured and disorganized, particularly for machine learning

Rule of Thumb: Before investing heavily in machine learning
solutions, ensure your data is in a state that's usable for machine learning.
If it's too structured, you may not need large language models. If it's very
unstructured, consider implementing an automated ETL (Extract, Transform, Load)
process to organize your data first.

Big Cloud Providers:

These acquisitions show that Snowflake and Databricks are
positioning themselves as important centers of data, even beyond the scope of
the big cloud providers like AWS, Google Cloud, and Microsoft Azure.

Rule of Thumb: Consider your needs beyond just data storage. These companies offer
unique capabilities that might provide value beyond what the big cloud
providers offer.

Lastly,let's talk about the winners and losers in this scenario. According to our discussions,
the real winners here are enterprises who will have more control over their
data and models, without having to send their data to potentially unregulated
environments. The potential losers? Companies that provide middleware for large
language models. Snowflake and Databricks are expected to develop their own
tools in this area, so if you're in this space, it's time to think about
differentiating your value proposition.

Theserules of thumb are not one-size-fits-all solutions, but they offer guidance.
It's essential to consider your organization's specific context and needs when
making strategic decisions. Let these recent acquisitions and the ensuing
discussion inform your choice, but ultimately, the best decision will depend on
your organization's unique requirements and goals.

Comparative Analysis: Snowflake vs Databricks - A Guide forStrategic Decision-Making

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Please note that this table is a simplification of the broader discussion and the decision to choose Snowflake or Databricks should be based on a thorough analysis of your specific business needs and context.

Snowflake's acquisition of Neeva:

  • TechCrunch: Snowflake acquires Neeva to bring intelligent search to its cloud data​
  • CRN: Snowflake Acquires Neeva, Brings Generative AI Search​
  • VentureBeat: Snowflake acquires Neeva, days after the search startup pivots​
  • TechTarget: Snowflake acquisition of Neeva to add generative AI​
  • NewsBytes: Snowflake acquires Google-challenger Neeva to improve its generative AI​

Databricks' acquisition of MosaicML: 6. TechCrunch: Databricks picks up MosaicML, an OpenAI
competitor, for $1.3B​​.

  • Fortune: Databricks says MosaicML deal is to ‘democratize A.I.'​
  • Reuters: Databricks strikes $1.3 billion deal for generative AI startup MosaicML​
  • US News: Databricks Acquires AI Startup MosaicML in $1.3 Billion Deal​
  • The Register: Databricks snaps up MosaicML to build private AI models​