IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
All articles
Pub/Sub to Dataflow to BigQuery: A Streaming Pattern
Pub/Sub receives events, Dataflow processes them and BigQuery stores them: the standard streaming pattern on Google Cloud. This guide says when you need all three, then runs the pipeline's logic locally on a test stream: a bad message routed aside, a duplicate removed, and a late event dropped or added.
BigQuery SQL: Tips, Tricks and Optimization
BigQuery rewards a few specific habits. This guide gives twelve, each tied to Google's own guidance: read fewer columns, filter and aggregate before joining, do several measures in one pass with COUNTIF, guard division with SAFE_DIVIDE, and stop expecting CTEs to be cached. Three are shown on 2.9 million real trips.
Partitioning and Clustering in BigQuery
Partitioning splits a BigQuery table into segments that a query can skip; clustering sorts the storage blocks inside them. This guide explains both, measures the effect with a model on 2.9 million real trip rows, shows why clustering column order matters, and lists the filters that quietly stop pruning.
BigQuery Cost Optimization: Slots, Pricing and Tips
A BigQuery bill has two parts, compute and storage, and nearly all the surprises are in compute. This guide explains both pricing models, prices three query shapes on a real table, gives the break-even between on-demand and slots, and lists the limits that stop one bad query becoming an expensive one.
BigQuery vs Snowflake vs Redshift: A Comparison
BigQuery, Snowflake and Amazon Redshift all separate storage from compute and all speak SQL. They differ in what you manage, what you are billed for and which levers you tune. This comparison is drawn from each vendor's documentation, with a model of how idle time drives the bill on time-based compute.
BigQuery ML: Machine Learning with SQL
BigQuery ML trains and runs machine learning models with SQL, on data that never leaves the warehouse. This guide covers the statements, model types and pricing, then reproduces a logistic regression locally on 300,000 real trips to show what the evaluation numbers mean and three ways an easy model can mislead.