Data Lake Analytics Training
Organisations have poured logs, transactions, documents and sensor feeds into cheap object storage, but much of it is never analysed because it sits in raw files rather than tidy warehouse tables. Over two weeks you learn to query that content directly with distributed SQL engines such as Spark SQL and Trino, working across CSV, JSON, Parquet and Avro files. Sessions explain storage zones from raw to curated, schema-on-read, table catalogues and open table formats such as Delta Lake and Apache Iceberg. You will combine structured and semi-structured data in one analysis, flattening nested fields and arrays along the way.
Notebook-based work in Python and SQL lets you explore large file-based datasets, build reusable transformations and visualise results. Performance modules cover partitioning, file sizing, columnar formats, predicate pushdown, caching and reading query plans to cut both runtime and cloud cost. Governance topics include access control, data quality checks and lineage. Data analysts, BI developers, data engineers and data scientists moving beyond warehouse-only environments will gain the most. You finish able to answer business questions straight from lake storage with queries that scale to very large volumes.
About this course
This course picks up where data lake storage leaves off, focusing on the analytics layer: querying data lakes with distributed SQL engines, combining structured and semi-structured sources, and building repeatable analysis notebooks over large file-based datasets.
Participants also cover performance tuning for lake queries, connecting BI tools to lake data, and validating results when data arrives in inconsistent formats. Exercises use multi-file, multi-format datasets that mirror how analytics teams actually encounter data lakes in practice.
Throughout the programme, participants work through realistic scenarios drawn from technology organizations, digital transformation programmes, data-driven enterprises, and public sector IT environments, examining how data lake analytics is applied in practice and where it most often breaks down. Delegates will develop practical skill in designing appropriate frameworks, selecting and applying the right tools for peer review and feedback, interpreting findings, and communicating conclusions clearly to colleagues, management and external stakeholders. Sessions move deliberately from principles to application, so that each technique introduced is practised against a realistic problem before the programme moves on — strengthening both individual effectiveness and the wider capability of the organizations delegates return to.
Training Objectives
By the end of this course, participants will be able to:
- Develop robust data lake analytics frameworks that support successful implementation and measurable outcomes.
- Analyze performance using recognized evaluation methods drawn from peer review and feedback.
- Design practical systems that strengthen accountability, transparency and evidence generation.
- Evaluate outcomes and assess longer-term impact across a range of operating contexts.
- Apply relevant tools, frameworks and analytical methodologies confidently and appropriately.
- Integrate alerting on data anomalies into existing organizational processes and reporting structures.
- Strengthen decision-making and operational effectiveness through evidence-based professional practice.
What you'll learn
Key Takeaways
By the end of the Data Lake Analytics Training, participants will leave with:
- A comprehensive working understanding of data lake analytics principles and performance-measurement methodology.
- Enhanced ability to design practical systems and evaluate outcomes reliably.
- Improved competency in data lake analytics, reporting structures and analytical interpretation.
- Practical techniques for translating findings into strategic insight and clear recommendations.
- Greater confidence in applying data lake analytics and assessing measurable results.
- Stronger capability to support organizational learning and continuous performance improvement.
- Improved approaches to strengthening sustainability, accountability and operational effectiveness.
Training Methodology
This training course adopts a practical, participant-centered learning approach that builds data lake analytics expertise, strengthens analytical capability and develops confident professional judgement. The methodology combines specialist facilitation, applied hands-on experience and globally recognized professional practice, so that delegates return able to apply what they have learned immediately rather than in theory only.
Methodology includes:
- Expert-led sessions facilitated by experienced data lake analytics practitioners and subject-matter specialists.
- Practical application of the frameworks, tools and techniques used in data lake analytics.
- Real-world case studies drawn from technology organizations, digital transformation programmes, data-driven enterprises, and public sector IT environments.
- Tool-based demonstrations of the systems, templates and reporting applications used in current practice.
- Interactive discussion and scenario-based learning focused on realistic challenges and decision points.
- Structured group exercises with facilitator feedback, reinforcing learning through practice rather than lecture alone.
1
- Data lake analytics landscape and common query engines
- Structured versus semi-structured data in a lake
- Setting up the lab environment and sample datasets
- First queries against lake-stored files
- Regulatory, ethical and compliance dimensions of 1
- Technology and automation supporting 1
- Reviewing outcomes and capturing lessons learned
2
- SQL fundamentals for distributed query engines
- Querying Parquet, JSON and CSV data directly
- Handling schema inconsistency across files
- Joining datasets stored in different formats
- Practical exercise and facilitated review
- Core principles and terminology underpinning 2
- Standards, frameworks and good practice governing 2
3
- Partition pruning and predicate pushdown concepts
- Writing efficient queries for large file sets
- Understanding query execution plans
- Workshop: optimising a slow lake query
- Practical tools, templates and techniques for 3
- Roles, responsibilities and stakeholder coordination in 3
- Common challenges, risks and failure points in 3
4
- Working with semi-structured and nested data
- Flattening and exploding nested fields
- Handling arrays and JSON structures in SQL
- Building clean analysis tables from raw lake data
- Documentation, record-keeping and reporting requirements
- Measuring performance and assuring quality in 4
- Worked example: applying 4 in a live data lake analytics context
5
- Notebook-based analysis workflows
- Combining SQL and Python/pandas for analysis
- Building reusable analysis functions
- Documenting analysis logic for reproducibility
- Core principles and terminology underpinning 5
- Standards, frameworks and good practice governing 5
- Practical tools, templates and techniques for 5
6
- Aggregations and window functions over lake data
- Time-series analysis using lake-stored data
- Building rolling and cumulative metrics
- Workshop: trend analysis on a sample dataset
- Roles, responsibilities and stakeholder coordination in 6
- Common challenges, risks and failure points in 6
- Documentation, record-keeping and reporting requirements
7
- Connecting BI tools to the data lake
- Designing semantic layers for business users
- Managing query costs in cloud data lake platforms
- Caching and materialised views for performance
- Measuring performance and assuring quality in 7
- Worked example: applying 7 in a live data lake analytics context
- Planning and sequencing 7 within the wider workflow
8
- Data validation and reconciliation techniques
- Handling late-arriving and duplicate data in analysis
- Building data quality checks into analytical queries
- Troubleshooting inconsistent results
- Data, evidence and information requirements for 8
- Decision points and escalation routes in 8
- Resourcing, cost and capacity considerations
9
- Automating recurring analytical queries
- Scheduling and orchestrating analysis jobs
- Alerting on data anomalies
- Scaling analysis as data volume grows
- Common challenges, risks and failure points in 9
- Documentation, record-keeping and reporting requirements
- Measuring performance and assuring quality in 9
10
- Capstone project: end-to-end analysis over a multi-format dataset
- Presenting findings and query design decisions
- Peer review and feedback
- Course wrap-up and further learning resources
- Worked example: applying 10 in a live data lake analytics context
- Planning and sequencing 10 within the wider workflow
- Data, evidence and information requirements for 10
This course suits professionals who need to extract insight from data lake environments, including:
- Data analysts and business intelligence developers
- Data engineers supporting analytics teams
- Analytics leads working with large, varied datasets
- Reporting and dashboard developers
- Data scientists who query lake-stored data
- IT Managers and Systems Administrators
- Software Developers and Engineers
On completion, participants receive a Vision Reach Global Consultancy Certificate of Completion. The course fee includes hands-on access to a cloud-based lab environment with sample lake datasets, course materials, refreshments and certificate processing. Facilitators have practical experience in large-scale data analytics.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
Need help with this booking?
Our training team can help with group pricing, invoicing, or picking the right schedule.
Choose Where You Want to Learn
Data Lake Analytics Training Venues
KenyaMombasaNext Nov 2· 13 dates$3,200- KenyaNaivashaNext Nov 9· 13 dates$3,000
- KenyaNairobiNext Nov 16· 13 dates$3,000
- KenyaKisumuNext Nov 2· 13 dates$3,000
- KenyaMachakosNext Nov 9· 13 dates$3,200
UgandaKampalaNext Nov 9· 13 dates$3,800
RwandaKigaliNext Nov 2· 13 dates$3,800
South AfricaCape TownNext Nov 2· 13 dates$7,600
TanzaniaZanzibarNext Nov 23· 13 dates$5,200
UAEDubaiNext Nov 2· 13 dates$7,600- South AfricaPretoriaNext Nov 9· 13 dates$6,400
- South AfricaJohannesburgNext Nov 9· 13 dates$6,800
- TanzaniaDodomaNext Nov 2· 13 dates$3,600
- TanzaniaDar es SalaamNext Nov 2· 13 dates$4,000
- TanzaniaArushaNext Nov 16· 13 dates$3,800
NigeriaAbujaNext Nov 16· 13 dates$5,600- NigeriaLagosNext Nov 23· 13 dates$5,600
GhanaAccraNext Nov 23· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Data Lake Analytics Training
Everything you need to know about this course before you register.
By the end of the Data Lake Analytics Training programme, you'll be able to query data lake content using distributed sql engines, combine structured and semi-structured data in a single analysis, build analysis notebooks over large file-based datasets, and optimise query performance for lake-scale data volumes. The full breakdown of topics is covered session by session in the Course Outline tab above.
This course suits professionals who need to extract insight from data lake environments, including: Data analysts and business intelligence developers, Data engineers supporting analytics teams, Analytics leads working with large, varied datasets, Reporting and dashboard developers, Data scientists who query lake-stored data, IT Managers and Systems Administrators, and Software Developers and Engineers.
Data Lake Analytics Training typically runs as 10 Days. It's available as in-person classroom training — every course can also be delivered on-site for your team on dates that suit you.
Data Lake Analytics Training is scheduled in-classroom in Mombasa, Kenya, Naivasha, Kenya, Nairobi, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Data Lake Analytics Training starts November 16, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Data Lake Analytics Training from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Data Lake Analytics Training is pitched at intermediate to advanced professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Data Lake Analytics Training vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Data Lake Analytics Training can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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Training by Country
Data Lake Analytics Training by Country
- Data Lake Analytics Training in GhanaGHClassroom in Accra
- Data Lake Analytics Training in KenyaKEClassroom in Mombasa, Naivasha, Nairobi, Kisumu, Machakos
- Data Lake Analytics Training in NigeriaNGClassroom in Abuja, Lagos
- Data Lake Analytics Training in RwandaRWClassroom in Kigali
- Data Lake Analytics Training in South AfricaZAClassroom in Cape Town, Pretoria, Johannesburg
- Data Lake Analytics Training in TanzaniaTZClassroom in Zanzibar, Dodoma, Dar es Salaam, Arusha
- Data Lake Analytics Training in UgandaUGClassroom in Kampala
- Data Lake Analytics Training in the UAEAEClassroom in Dubai
- Live Online Data Lake Analytics TrainingInstructor-led, joined from anywhere
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