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Data Engineering
Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
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I am preparing for the data analyst exam and I need as many resources as I can get to fully prepare. Hands-on labs will be welcome as well

boyelana
Contributor III

I am preparing for the data analyst exam and I need as many resources as I can get to fully prepare. Hands-on labs will be welcome as well

9 REPLIES 9

Hubert-Dudek
Esteemed Contributor III

yes refer the databricks documentation , you will easily get the proper learning plan

Rishabh Pandey

Aviral-Bhardwaj
Esteemed Contributor III

this is amazing ,thanks

AviralBhardwaj

UmaMahesh1
Honored Contributor III

Best resource is the one databricks training academy is providing. You can clear easily using that alone.

Uma Mahesh D

Ajay-Pandey
Esteemed Contributor III

Please refer below topics that may help you in exam-

  • Describe Databricks SQL and its capabilities, including:
    • Databricks SQL (users, benefits, queries, dashboards, compute)
    • Integrations (Partner Connect, data ingestion, other BI tools)
    • Lakehouse (medallion architecture, streaming data)
  • Manage data with Databricks tools and best practices, including:
    • Delta Lake (basics, benefits)
    • Storage and Management (tables, databases, views, Data Explorer)
    • Security (table ownership, PII data)
  • Use Structured Query Language (SQL) to complete tasks in the Lakehouse, including:
    • Basic SQL (basic query structure, combining data, aggregations)
    • Complex Data (nested data objects, roll-ups, windows, cubes)
    • SQL in the Lakehouse (ANSI SQL, working with silver-level data, query history, higher-order functions, user-defined functions)
  • Create production-grade data visualizations and dashboards, including:
    • Visualization (Databricks SQL capabilities, types of visualizations, storytelling with data)
    • Dashboarding (Databricks SQL capabilities, parameterized dashboards and queries, sharing)
    • Production (refresh schedules, query alerts)
  • Develop analytics applications to solve common data analytics problems, including:
    • Descriptive Statistics (discrete statistics, summary statistics)
    • Common Applications (data enhancement, data blending, last-mile ETL)

Ajay Kumar Pandey

tunstila
Contributor II

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