Apache Spark is an open-source engine for processing large datasets in parallel across a cluster of machines. Teams use it for batch jobs, streaming and machine learning.
Batch processing is handling data in scheduled groups, such as hourly or nightly runs, rather than processing each record the moment it arrives. Payroll runs, overnight warehouse loads and monthly billing are common examples, where completeness matters more than instant results.
Big data refers to datasets too large, fast-moving or varied to store and process efficiently on a single traditional database or server. A retailer analysing clickstream events from millions of visitors to tune product recommendations is a typical case, using tools that spread the work across many machines.
A data lakehouse combines the cheap, flexible storage of a data lake with warehouse features such as tables, transactions and fast SQL. Both kinds of work run on the same copy of the data.
A Python developer writes software in Python, a general-purpose language used for web backends, automation, data pipelines, machine learning and scripting. Before hiring one, decide which of those areas the job actually needs.
A data lake is a large, low-cost store that holds raw data in its original format, structured or not, until someone needs to process or analyse it.
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