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.
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Building a Real-Time Dashboard with GCP
A real-time dashboard is a chain, and it is as fresh as its slowest link. This guide builds one on Google Cloud, from events in Pub/Sub to a per-minute table in BigQuery and a report in Data Studio, and replays an hour of real events to show why recent numbers change after they first appear.
Time Intelligence in DAX: YTD, MTD and Comparisons
DAX time intelligence for Power BI: the date table it depends on, year-to-date and month-to-date measures, fiscal year ends, prior-year comparisons with SAMEPERIODLASTYEAR and DATEADD, rolling windows with DATESINPERIOD, and the partial-month trap that turned a 62.7% increase into an 8.6% fall, shown with real numbers.
Amazon Kinesis Explained: Real-Time Streaming
Amazon Kinesis is AWS's family of streaming services. This guide explains Kinesis Data Streams, shards, partition keys, sequence numbers and retention, shows on 2,000 real taxi events how the choice of partition key spreads or skews load across shards, and covers capacity modes, consumer types and where Firehose fits.
Time Series Forecasting: A Beginner-Friendly Introduction
A beginner-friendly introduction to time series forecasting on 36 years of real monthly CO2 measurements: what trend and seasonality are, why simple benchmark methods matter, how Holt-Winters combines both patterns, and how to test forecasts honestly by repeatedly forecasting the next year from past data only.
Batch vs Real-Time Model Inference: Tradeoffs
Nightly batch scoring is cheap and simple; predicting on request is fresh but costlier. This guide measures both on six months of hourly bike rentals, where nightly scoring with midnight's weather had 42% more error than using the current hour's, and sets out when each pattern fits.
Real-Time Analytics: Architecture and Tools
How real-time analytics systems are built: the four layers of ingestion, stream processing, serving and consumption, the tools at each layer including Kafka, Spark, Flink and real-time OLAP databases such as Druid, Pinot and ClickHouse, latency budgets, common architectures, and the pitfalls that raise costs.