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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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Data Lake vs Data Warehouse vs Lakehouse
A clear comparison of data lakes, data warehouses and lakehouses: what each stores and when structure is applied, strengths and failure modes such as the data swamp, what a lakehouse adds through table formats and governance, a side-by-side table, and practical guidance on choosing or combining them.
Azure Data Lake Storage Gen2 Explained
Azure Data Lake Storage Gen2 is Blob Storage with a hierarchical namespace switched on. This guide explains what that one setting changes, how role assignments and access control lists are evaluated together, with a model checked against Microsoft's own table, and how to lay out folders, files and tiers.
Apache Iceberg vs Delta Lake vs Hudi
A comparison of the three open table formats: the problems they all solve on a data lake, Iceberg's hidden partitioning and partition evolution, Delta Lake's transaction log and Spark roots, Hudi's copy-on-write and merge-on-read tables and incremental queries, interoperability through UniForm, and a choice guide.
Delta Lake Explained: Reliable Lakes with ACID
Delta Lake explained with a working table built from real taxi data: what the transaction log is, how each write becomes an atomic version, schema enforcement rejecting a bad append, delete and merge, a merge that quietly re-inserted deleted rows, time travel between versions, and maintenance.
AWS Lake Formation: Governance for Data Lakes
AWS Lake Formation puts database-style grants on top of an S3 data lake. This guide explains the problem it solves, how a query is authorised through credential vending, column, row and cell-level filters, tag-based access control, the default that leaves its permissions inactive, and how to adopt it gradually.
Building a Data Lake on AWS with S3 and Glue
A step-by-step guide to building a data lake on AWS: how to lay out an S3 bucket in zones, land raw data unchanged, write a cleaned Parquet layer, and register tables in the Glue Data Catalog, shown end to end on a real 2.96-million-row file, followed by querying, security, cost controls and how lakes become swamps.