Category Archives: Technology
For readers studying Databricks Data Engineer Associate, Unity Catalog row filters let Databricks apply a policy function to each row and return only the rows a user is authorized to see. The stored table can remain shared while the visible row set changes according to identity, group membership, tags, or other policy inputs. This is […]
Databricks Clean Rooms are designed for organizations that need to analyze or combine data across organizational boundaries without giving each participant unrestricted access to the other participants’ raw datasets. The clean room creates a controlled collaboration environment in which approved data, notebooks, and computations can be made available under rules that all parties understand. The […]
Databricks administrators have long used cluster policies to put guardrails around how users create Spark clusters. In the current product interface and documentation, the preferred name is compute policies. The older “cluster policy” label still appears in API and CLI surfaces, so both terms matter operationally: the concept has evolved, but existing automation does not […]
A Databricks compute policy becomes valuable when it expresses a workload decision that the organization has already made. The policy should answer questions such as: which runtime families are supportable, which access modes are allowed, how large may a cluster become, which tags are mandatory, which libraries are trusted, and how much hourly cost can […]
For Databricks Data Engineer Professional candidates, deletion vectors change the cost model of row-level modification in Delta Lake. Without them, changing or deleting even a small number of rows can require rewriting the Parquet files that contain those rows. With deletion vectors enabled, Databricks can record which rows are logically removed or replaced in metadata […]
When preparing for Databricks Data Engineer Professional, optimized writes address a simple but expensive problem in distributed data engineering: a parallel job can produce far more files than downstream readers want. Every task may write into multiple partitions, and a large shuffle can leave a table with thousands of small objects. The data is correct, […]
VACUUM is one of the most consequential maintenance operations in Delta Lake because it turns logical history into physical deletion. A Delta table can stop referencing old data files long before those files disappear from object storage. That delay is intentional: it supports time travel, rollback, long-running readers, and recovery from recent mistakes. VACUUM removes […]
For Databricks Data Engineer Professional candidates, Databricks renamed Delta Sharing to OpenSharing in June 2026 following the release of the open-source OpenSharing standard. The queue topic “Delta Sharing Governance” still reflects terminology that many teams, integrations, and runbooks use, so the useful way to handle the transition is to connect the names rather than pretend […]
When preparing for Databricks GenAI Engineer Associate, the Databricks feature historically called Mosaic AI Agent Evaluation now sits inside the broader MLflow 3 generative-AI workflow. That change is more than branding. Current Databricks guidance exposes evaluation through the mlflow.genai namespace, combines offline testing with tracing and production monitoring, and treats evaluation datasets, scorers, human feedback, […]
For readers studying Google Cloud Generative AI Leader, Vertex AI Feature Store has changed substantially from the first generation of the product. In Google’s 2026 platform naming, the capability is documented as Feature Store on Gemini Enterprise Agent Platform. More importantly, the current architecture centers on features prepared in BigQuery and registered through a metadata […]
Google Cloud’s current agent platform naming changed materially in April 2026. Vertex AI became part of Gemini Enterprise Agent Platform, Agent Builder moved into that platform, Agent Engine became Agent Runtime, Agent Builder Sessions became Agent Platform Sessions, and Memory Bank became Agent Platform Memory Bank. Agent Builder in Generative AI Leader should be evaluated […]
Grounding Gemini with enterprise data connects model responses to information controlled by the organization instead of asking the model to rely only on its learned parameters. Current Gemini Enterprise Agent Platform documentation supports grounding through enterprise search capabilities such as Agent Search and through external search APIs, allowing applications to retrieve private evidence at generation […]
For Google Cloud Generative AI Leader candidates, Google’s managed agent hosting service has changed names. What many teams still know as Vertex AI Agent Engine is now documented as Agent Runtime on Gemini Enterprise Agent Platform. The rename matters because architecture diagrams, Terraform modules, SDK examples, console labels, and older runbooks can use different generations […]
Grounding Gemini with Google Search connects generation to public web information so the model can answer questions that depend on current events, newly published material, changing products, or facts outside the application’s private knowledge base. The grounding response includes metadata that applications can use to present supporting sources and Search Suggestions. Google Search grounding in […]
When preparing for Google Cloud Generative AI Leader, batch inference is the right inference pattern when an application needs predictions for a large body of data but does not need an interactive response for each record. Google’s 2026 platform naming now presents this capability as Gemini Enterprise Agent Platform batch inference in newer documentation, while […]
