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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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Pub/Sub Explained: Messaging and Streaming on GCP
Pub/Sub carries messages from the systems that produce events to the systems that use them, without either knowing about the other. This guide explains topics, subscriptions and the three delivery types, models why a message can arrive more than once, and covers ordering, dead-letter topics, retention and cost.
Apache Kafka Explained: Streaming Data Basics
Apache Kafka explained from the ground up: events, topics and partitions, why keys decide ordering, how consumer groups track offsets and why data is not deleted when read, each shown by producing 1,000 real taxi events to a running broker and reading them back, plus replication and delivery guarantees.
Batch vs Streaming Data Processing: A Clear Comparison
Batch and streaming data processing compared honestly: what each is, a measurement on a real day of 77,033 taxi trips showing what a nightly, hourly, five-minute and per-event pipeline would each know at 12:30, where micro-batching fits, what streaming costs in complexity, and how to choose.
Building a Streaming Pipeline with Kafka and Spark
A streaming pipeline built and run end to end: 77,033 real taxi events on a Kafka topic, read by Spark Structured Streaming, parsed from JSON, aggregated into hourly event-time windows with a watermark and written to a sink, with the real output showing why the final window is not emitted, plus what production adds.
Streaming Data into BigQuery: A Practical Guide
Streaming new events into BigQuery is well covered. Streaming changes, where rows are updated and deleted, needs BigQuery's change data capture. This practical guide covers the pseudocolumns, primary keys, sequence numbers and max_staleness, and simulates the ordering rules to show how out-of-order changes go wrong.
Streaming Data with Kinesis to S3 and Redshift
A practical guide to landing streaming data on AWS: Kinesis Data Streams into S3 with Firehose, how size-or-time buffering decides file counts, shown on a real day of 77,033 events, converting to Parquet and partitioning on the way, the two routes into Redshift, duplicates, and the compaction most streams still need.