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- The AI Data Stack is Evolving: Migrations, Real-Time AI, and Analyst Acceleration
The AI Data Stack is Evolving: Migrations, Real-Time AI, and Analyst Acceleration
Biweekly Data & Analytics Digest: Cliffside Chronicle
Josh Miramant
26 Feb • Estimated Reading Time: 6 minutes
Why These 3 Major Databricks Announcements Matter
Databricks is making strategic moves to solidify its position as the go-to data and AI platform. Three major announcements—the acquisition of BladeBridge, deeper integration with Confluent for real-time AI, and the launch of SAP Databricks—highlight a multi-pronged approach to expanding its ecosystem.
These three announcements collectively reinforce Databricks’ strategy to dominate the enterprise AI and data space. By simplifying legacy migrations, enabling real-time AI, and unlocking SAP data, Databricks is accelerating AI adoption at scale—making data-driven decision-making faster, more cost-effective, and widely accessible.
Data Engineering Demand Remains High
Despite a choppy tech job market overall, data engineers remain in high demand. In the latest monthly jobs report, data engineer openings jumped 42% month-over-month…one of the fastest-growing categories in tech hiring. Data roles (including analysts and engineers) are among the most sought-after skill sets, with major staffing firms listing data engineer as a top tech role for 2025. At the same time, the broader tech sector isn’t exactly booming – January saw about 6,000 tech employees laid off, nearly 3× the number from December.
For data team leaders, the takeaway is to continue investing in talent development and retention: the right skills (cloud data pipelines, streaming, ML engineering) are in demand, but candidates might also be weighing stability due to recent layoff news. In short, it’s a competitive hiring climate – data engineers have plenty of opportunities, yet both candidates and employers are doing due diligence.
Migration to Databricks: GetYourGuide’s Data Shift
GetYourGuide recently streamlined its data infrastructure by moving from Snowflake to Databricks, reducing operational costs by 20% while maintaining BI performance. The decision came from a need to simplify an increasingly complex architecture. Managing both Snowflake for BI queries and Databricks for data processing added unnecessary overhead. Consolidating everything onto Databricks created a more efficient and cost-effective workflow.
The migration was carefully planned to ensure Looker continued delivering fast insights without disruption. Now, with a unified data platform, GetYourGuide benefits from lower costs, simplified operations, and a system built for long-term scalability. This shift is a prime example of how optimizing data architecture can drive efficiency without compromising performance.
Some great learnings are listed at the end:
Aim for an incremental rollout so that you can parallelize migration workloads. For our migration, the following components helped us distribute the migration of single Looker models:
Assign ownership to individual engineers for separate workloads (end to end, including bug fixing post-roll out)
ThoughtSpot Launches Analyst Studio
ThoughtSpot (known for its search-based analytics) just launched Analyst Studio, an all-in-one “creator” workspace for data analysts built to work with Snowflake, Google BigQuery, and Databricks. The goal is to let analysts connect and mash up data from various sources, do ad-hoc analysis and advanced data science in one place, and even manage cloud costs and performance. It comes with an integrated SQL/Python/R notebook environment and AI-assisted query writing.
ThoughtSpot is positioning this as a tool to prepare “AI-ready” data and streamline analytics workflows within a single platform – potentially reducing the need to jump between BI tools, SQL editors, and Jupyter notebooks. Data teams at mid-market companies might find this interesting for boosting analyst productivity and ensuring consistency across tools.
Pipelines to Anywhere – New Delta Live Tables “sinks”
Databricks has introduced Delta Live Tables (DLT) “sinks”, a feature that allows users to write data directly from DLT pipelines into Unity Catalog managed tables. This eliminates the need for manual ETL processes, making it easier to manage governed, production-grade data pipelines.
The key benefit is seamless integration with Unity Catalog, ensuring that all data remains structured, discoverable, and compliant with governance policies. This helps organizations maintain lineage, enforce access controls, and optimize query performance while reducing operational overhead.
For enterprises scaling AI and analytics, this update simplifies data pipeline orchestration, enhances reliability, and accelerates time to insight—all while reducing the complexity of managing multiple data systems.
Blog Spotlight: The Best Data Warehouse
The exponential increase in data variety, veracity, and volume brings the challenge of effectively storing, sorting, manipulating, and driving decisions using this data. Data warehouses, data lakes, and data lakehouses are the different data management architectures, each with their share of benefits and challenges.
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