Lesson proof
Concept, demo, checklist, lab, and assignment evidence.
Big Data / Cloud Analytics / Big Data Analyst
Big Data Analytics
Build practical big data analytics skills across distributed data processing, data lakes, warehouses, Spark, streaming, governance, and cloud analytics platforms.
Big data platform readinessSpark and lakehouse practiceAnalytics pipeline portfolio
Start domain mock testPlatform-wide module outputs
Every module now feeds portfolio proof and CV readiness.
Portfolio pack
Requirement, artifact, validation, risk note, and interview story.
CV signal
Role-specific skill statement linked to a score or artifact.
Review queue
Submitted evidence can support dashboard, readiness, and career exports.
Open materials
Databricks Certified Data Analyst AssociateLesson + portfolio pack + CV evidenceBig data foundationsLesson + portfolio pack + CV evidenceData lakes and lakehouse architectureLesson + portfolio pack + CV evidenceApache Spark analyticsLesson + portfolio pack + CV evidenceBatch and streaming pipelinesLesson + portfolio pack + CV evidenceCloud data warehousesLesson + portfolio pack + CV evidenceData governance and qualityLesson + portfolio pack + CV evidencePerformance and cost optimizationLesson + portfolio pack + CV evidence
Certification objective coverage
Every provider-aligned module is connected to a lesson, labs, mock questions, and implementation proof.
This is the track-level audit view for blueprint alignment. Exact exam wording should still be checked against the current official provider guide before public exam-code claims are made.
big-data-analytics.databricks-certified-data-analyst-associate.01 / 13% weight
Apply Databricks Certified Data Analyst Associate decisions to Big Data Analyst scenarios
Implementation proof
- Define Databricks Certified Data Analyst Associate in plain language and explain the provider service family it belongs to.
- Show how Databricks Certified Data Analyst Associate is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
big-data-analytics.big-data-foundations.02 / 13% weight
Apply Big data foundations decisions to Big Data Analyst scenarios
Implementation proof
- Define Big data foundations in plain language and explain the provider service family it belongs to.
- Show how Big data foundations is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
big-data-analytics.data-lakes-and-lakehouse-architecture.03 / 13% weight
Apply Data lakes and lakehouse architecture decisions to Big Data Analyst scenarios
Implementation proof
- Define Data lakes and lakehouse architecture in plain language and explain the provider service family it belongs to.
- Show how Data lakes and lakehouse architecture is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
big-data-analytics.apache-spark-analytics.04 / 13% weight
Apply Apache Spark analytics decisions to Big Data Analyst scenarios
Implementation proof
- Define Apache Spark analytics in plain language and explain the provider service family it belongs to.
- Show how Apache Spark analytics is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
big-data-analytics.batch-and-streaming-pipelines.05 / 13% weight
Apply Batch and streaming pipelines decisions to Big Data Analyst scenarios
Implementation proof
- Define Batch and streaming pipelines in plain language and explain the provider service family it belongs to.
- Show how Batch and streaming pipelines is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
big-data-analytics.cloud-data-warehouses.06 / 13% weight
Apply Cloud data warehouses decisions to Big Data Analyst scenarios
Implementation proof
- Define Cloud data warehouses in plain language and explain the provider service family it belongs to.
- Show how Cloud data warehouses is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
big-data-analytics.data-governance-and-quality.07 / 13% weight
Apply Data governance and quality decisions to Big Data Analyst scenarios
Implementation proof
- Define Data governance and quality in plain language and explain the provider service family it belongs to.
- Show how Data governance and quality is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
big-data-analytics.performance-and-cost-optimization.08 / 9% weight
Apply Performance and cost optimization decisions to Big Data Analyst scenarios
Implementation proof
- Define Performance and cost optimization in plain language and explain the provider service family it belongs to.
- Show how Performance and cost optimization is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
Test readiness
Mock test by domain
Practice every domain in this track with exam-style questions, answer keys, and explanations.
Open mock testMost in-demand certification materials
High-value certificates connected to this track.
Google Cloud
Google Professional Data Engineer
Needs reviewLast verified: Not verifiedNext review: Provider source review required
Data engineers and big data analysts building pipelines, warehouses, governance, and analytics platforms.
Google Professional Data Engineer is mapped to platform lessons and labs, but still needs a dated official-source review.
- Data pipeline architecture checklist
- BigQuery and cloud data warehouse scenario notes
- Governance, quality, lineage, and cost review workbook
Databricks
Databricks Certified Data Analyst Associate
Needs reviewLast verified: Not verifiedNext review: Provider source review required
Analysts working with lakehouse analytics, SQL warehouses, dashboards, and governed data products.
Databricks Certified Data Analyst Associate is mapped to platform lessons and labs, but still needs a dated official-source review.
- Lakehouse vocabulary and workflow notes
- Spark and SQL analytics lab checklist
- Dashboard and governed table practice tasks
Certification provider connections
Connect this learning path to the official exam provider.
Google Cloud Certification
Google Professional Data Engineer
Review the certification path, confirm the exam language and delivery option, then attach the target date to the learner study plan.
Booking partner: Google Cloud exam registration partner
- Create or confirm the Google Cloud certification profile.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
Certification provider
Databricks Certified Data Analyst Associate
Confirm the official provider, exam code, delivery rules, ID policy, and reschedule window before booking.
Booking partner: Provider exam partner
- Create or confirm the Provider learner account.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
01 Match
Map each Daskerel track to the official provider, exam code, registration page, and verification route.
02 Prepare
Use provider objectives with Daskerel lessons, mock exams, labs, and evidence packs before booking.
03 Book
Send learners to the official scheduling partner while keeping target dates and next actions in the dashboard.
04 Verify
Capture certificate URL, badge, expiry, renewal plan, and portfolio proof after the learner passes.
Study plan
Start with the difference between transactional data, analytical data, data lakes, warehouses, lakehouses, batch, and streaming.
Practise Spark-style transformations, partitioning, file formats, and pipeline evidence before deeper platform tuning.
Connect analytics outputs to governance, quality checks, cost controls, and business decisions.
Hands-on labs
Design a data lake ingestion flow from raw files to curated analytics tables.
Build a Spark notebook that cleans, groups, and summarizes a large dataset.
Create a pipeline evidence checklist covering quality, lineage, partitioning, monitoring, and cost.
Track learning assets
Templates and revision tools for this path.
Exam blueprint checklistBig Data Analytics
Weekly study plannerBig Data Analytics
Command and service cheat sheetBig Data Analytics
Architecture pattern cardsBig Data Analytics
Flashcard revision setBig Data Analytics
Mock exam review sheetBig Data Analytics
Lab evidence templateBig Data Analytics
Interview story builderBig Data Analytics
Portfolio project rubricBig Data Analytics
Final readiness checklistBig Data Analytics
Practice questions
Why do big data platforms often use columnar file formats such as Parquet?
Columnar formats reduce scan cost and improve analytical query performance because engines can read only the columns needed for a query.
What is the difference between batch and streaming analytics?
Batch analytics processes data in scheduled chunks, while streaming analytics processes events continuously or near real time.