Big Data Analytics with Python Training Course

Participants can structure a reproducible Python analytics project with controlled dependencies and credentials.

• Conda environments pin Python and package versions for reproducible projects
• Jupyter notebooks separate exploration, narrative and executable analysis cells
• Project folders organise source code, tests, configuration and data contracts
• Git branches, commits and pull requests support reviewable analytical code
• Requirements files record dependencies for Spark and local Python execution
• Secrets management keeps database credentials outside notebooks and repositories

Participants can ingest common source formats while recording schema, extraction and lineage information.

• CSV, JSON, Parquet and Avro formats compared for analytical workloads
• Schema-on-read parsing handles nested records, arrays and nullable fields
• Read partitioned Parquet datasets with predicate pushdown and column pruning
• JDBC ingestion extracts relational tables using incremental watermark queries
• REST API pagination, retries and rate limits managed in Python
• Data lineage records source, extraction time, schema and owner

Participants can prepare and profile moderate-sized datasets using efficient pandas methods.

• pandas DataFrames inspect types, missingness, duplicates and cardinality
• Vectorised filtering, joins and groupby aggregations replace row-wise loops
• Datetime parsing standardises time zones, frequencies and reporting periods
• Categorical encoding reduces memory use and preserves controlled values
• Reusable functions turn exploratory transformations into testable Python modules
• Profile reports expose outliers, invalid codes and unexpected distributions

Participants can define, test and document data-quality controls for analytical pipelines.

• Define data-quality dimensions: completeness, validity, uniqueness, consistency and timeliness
• Great Expectations suites express column, schema and distribution checks
• Quarantine invalid records while retaining reasons, timestamps and source references
• Null handling distinguishes unknown, not applicable, delayed and corrupted values
• Reconciliation compares source totals, record counts and key aggregates
• Document assumptions, transformations and exceptions in a data-quality log

Participants can explain Spark execution and configure a PySpark application for distributed processing.

• SparkSession configuration connects Python applications to a Spark cluster
• RDDs, DataFrames and Spark SQL compared by abstraction and use
• Lazy evaluation builds directed acyclic graphs before execution begins
• Transformations create new datasets; actions trigger computation and materialisation
• Executors, drivers and cluster managers determine resource allocation
• Explain plans reveal scans, exchanges, joins and predicate placement

Participants can create distributed transformations, joins and aggregations using PySpark DataFrames.

• PySpark DataFrames select, filter, derive and rename distributed columns
• Built-in functions replace Python UDFs when optimiser support exists
• Window functions calculate ranks, lags, rolling metrics and cohorts
• Join types, join keys and duplicate multiplication diagnosed systematically
• Aggregation plans produce customer, product and time-period metrics
• Column expressions preserve distributed execution without collecting data locally

Participants can use Spark SQL to develop readable and efficient analytical transformations.

• Spark SQL creates temporary views for analyst-friendly transformation logic
• Common table expressions structure multi-stage analytical queries clearly
• GROUP BY, HAVING and window clauses answer operational questions
• Query plans identify full scans, skewed joins and unnecessary shuffles
• Partition filters and column pruning reduce scanned data volume
• SQL and DataFrame implementations compared for maintainability and performance

Participants can design scalable analytical storage with appropriate file, partition and table choices.

• Parquet compression, row groups and statistics support selective reads
• Partition design uses date, region and domain keys judiciously
• Small-file compaction improves scheduler efficiency and metadata performance
• Delta Lake transactions prevent partial writes and concurrent update conflicts
• Medallion layers define raw, validated and serving dataset responsibilities
• Retention policies balance reproducibility, storage cost and regulatory obligations

Participants can diagnose Spark performance issues and apply targeted optimisation techniques.

• Partition counts align task parallelism with available cluster cores
• Repartition and coalesce manage shuffle cost and output file sizes
• Broadcast joins avoid large data exchanges for small dimensions
• Salting techniques mitigate skewed keys in high-volume joins
• Cache only reused datasets after measuring memory and recomputation cost
• Spark UI metrics diagnose stages, spills, skew and executor failures

Participants can build parameterised batch jobs with testing, audit and recovery controls.

• Parameterised PySpark jobs separate business rules from runtime configuration
• Idempotent writes permit safe reruns after partial pipeline failures
• Watermarks identify new records for incremental batch processing
• Control tables store run status, source offsets and audit counts
• Structured logging captures job identifiers, timings, exceptions and record volumes
• Unit tests verify transformations using representative edge-case datasets

Participants can define orchestration, monitoring and support arrangements for production data jobs.

• Apache Airflow DAGs express task dependencies, schedules and retry policies
• Task parameters pass dates, paths and environment-specific settings safely
• Service-level objectives define freshness, completion time and failure thresholds
• Alert routing distinguishes transient errors from business-critical data incidents
• Runbooks specify diagnosis steps, rollback decisions and escalation contacts
• Backfill plans prevent duplicate outputs during historical reprocessing

Participants can explain and configure core Structured Streaming patterns for event data.

• Structured Streaming processes append, update and complete output modes
• Event-time windows aggregate transactions despite delayed message arrival
• Watermarks bound state size while handling acceptable late events
• Checkpoint locations preserve progress across application restarts and failures
• Kafka topics, partitions and consumer offsets underpin streaming ingestion
• Deduplication keys control repeated events within defined time horizons

Participants can design analytical tables and features that preserve business meaning and historical accuracy.

• Feature tables combine behavioural, transactional and reference data at grain
• Dimensional modelling defines facts, dimensions, surrogate keys and conformed attributes
• Slowly changing dimensions preserve historical values using Type 2 records
• Point-in-time joins prevent future information entering predictive features
• Data leakage checks protect model evaluation from invalid training signals
• Business metrics translate model outputs into measurable operational decisions

Participants can prepare features, train basic Spark models and interpret evaluation results.

• Spark ML Pipelines chain preprocessing, modelling and scoring stages
• StringIndexer, OneHotEncoder and VectorAssembler prepare structured feature vectors
• Train-validation splits preserve temporal ordering where outcomes evolve
• Logistic regression produces calibrated probabilities for binary classification
• Regression metrics include MAE, RMSE and residual diagnostics
• Classification evaluation uses precision, recall, F1 and ROC-AUC appropriately

Participants can apply governance controls to analytical data, access decisions and model outputs.

• Role-based access controls restrict datasets according to approved job functions
• PII classification identifies direct identifiers, quasi-identifiers and sensitive attributes
• Tokenisation, hashing and masking support controlled analytical data access
• GDPR principles guide purpose limitation, minimisation and retention decisions
• Model documentation records intended use, limitations, inputs and monitoring
• Bias checks compare error rates across relevant protected groups

Participants can turn analytical outputs into documented findings for technical and business stakeholders.

• Aggregate outputs into decision-ready KPI tables with stated metric definitions
• Matplotlib and seaborn charts expose trends, variation and outliers
• Confidence intervals communicate uncertainty around sampled or modelled estimates
• Data dictionaries define fields, grain, refresh cadence and ownership
• Executive narratives state findings, caveats, decisions and recommended actions
• Acceptance criteria link delivered datasets to stakeholder requirements

Participants can build, review and present a complete PySpark analytics pipeline with validated outputs, controls and handover documentation.

• Frame a business question, decision owner, scope and success measures
• Acquire and profile source data using documented schema and lineage
• Build a PySpark transformation pipeline with quality controls and audit outputs
• Optimise storage, joins and partitions using observed execution evidence
• Produce validated metrics or model scores with documented limitations
• Present architecture, findings, controls and operational handover recommendations