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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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VPC Basics for Data Engineers on AWS
Data engineers meet Amazon VPC when a job cannot reach a database or times out calling S3. This guide covers the networking behind those failures: CIDR blocks and subnets, what makes a subnet public or private, route tables, NAT gateways, VPC endpoints and security groups, with a small platform network built in code.
Pydantic Explained: Data Validation in Python
Pydantic v2 shown on one realistic job, validating a payment webhook: how models convert and check types, field constraints, custom field and model validators, the structured errors it reports all at once, strict mode, parsing and producing JSON, and validating lists with TypeAdapter.
Python Data Structures: A Complete Reference
A reference to Python's data structures organised by the job each does best: the four built-ins, then deque for queues, Counter for counting, defaultdict for grouping, heapq for priorities and top-k, bisect for sorted lookups and namedtuple or dataclass for records, with the cost of each common operation.
Parameterizing Pipelines in Azure Data Factory
Parameters turn a pipeline that copies one table into a pipeline that copies any table. This guide explains the four places parameters live in Azure Data Factory, how a value travels from a trigger down to a connection, a worked generic copy in JSON, and where parameterising goes too far.
Loading Data into Synapse with ADF
Loading a Synapse SQL pool row by row is slow by design. This guide explains the three load methods the Azure Data Factory Copy activity offers, the COPY statement, PolyBase and bulk insert, when a load is direct and when it needs staging, the settings for each, upserts, and the documented limits.
A/B Testing for Data Scientists: A Practical Guide
A/B testing from design to decision: one primary metric, calculating the sample size before launch, analysing conversion with a two-proportion test and a confidence interval, checking for sample ratio mismatch, and avoiding the traps (peeking, many metrics, novelty) that produce confident wrong answers.