Data-Storage

Data Storage for Big Data: Lakes, Warehouses, and Lakeshouse Big data teams face a common question: how to store large amounts of data so it is easy to analyze. The choices are data lakes, data warehouses, and the newer lakehouse. Each pattern has strengths and limits, and many teams use a mix to stay flexible. Data lakes store data in its native form. They handle logs, images, tables, and files. They are often cheap and scalable. The idea of schema-on-read means you decide how to interpret the data when you access it, not when you store it. Best practices include a clear metadata catalog, strong access control, and thoughtful partitioning. Example: a streaming app writes JSON logs to object storage, and data engineers index them later for research. ...

Time Series Databases for Real-World Monitoring Time series data comes from devices, apps, and services. A time series database (TSDB) stores data with timestamps in a compact, efficient layout. For real-world monitoring, you need fast writes, durable storage, and quick queries across recent time windows. When choosing a TSDB, look at ingestion rate, memory and disk use, scalability, and how easy it is to set retention and downsampling. High cardinality (many unique series) can hurt performance, so test your workload. Decide on a data model: do you prefer labels and tags, or a SQL table with time context? ...

From Ingestion to Insight: Building Reliable Data Pipelines Data science pipelines turn raw data into actionable knowledge. They connect multiple steps—from data sources to dashboards—so decisions come from fresh, trustworthy facts. A well built pipeline is reliable, reproducible, and easy to extend as needs change. Data ingestion gathers data from databases, logs, APIs, and files. It often mixes batch loads with streaming events. A simple rule is to validate structure at the edge: check fields, types, and missing values as data arrives. Designing for schema drift helps you adapt when sources change. ...