1. I think in the first instance, Excel performs type-inference and coerces it into a numeric type and writes a truncated precision version of numeric fields in CSV, which is an abominable sin. There are workarounds for this [1], but agree this is terrible behavior due to CSV's lack of types.
Arguably, there should have been an explicit Excel switch which forces all CSV data to be parsed as raw strings.
I've seen problems with Excel CSVs that are worse than that: I have serial numbers that have leading 0's that have semantic meaning, like 000002324122323. Most CSV parsers cannot tell that this isn't a numeric type and so handle it wrongly. So I resort to [1].
2. Big data is of course somewhat ambiguous nomenclature as well, but is nowadays typically understood to mean the Hadoop ecosystem or similar. Data is typically ingested into a distributed file system (HDFS, S3, etc.) in formats such as Parquet, Avro, JSON and often CSV. A schema-on-read database like Hive sits on top of this layer and presents a SQL interface to the user. Tools like Apache Spark provide programmatic transformations that operate on the data on the large.
CSV is often promoted as a format for storing structured data due to ease of ingestion and inspection (all you need is a text editor for troubleshooting). However, you pay a performance penalty every time an analytic query is run because CSV doesn't support indexes, predicate pushdowns, compression, etc. and records can sometimes be uninterpretable under certain schemas (in which case they are simply excluded or dropped).
Sometimes having one too many commas in a row can completely mess things up (all the columns get shifted in a record) -- I found this out the hard way when Spark exported malformed CSVs with un-escaped commas.
[1] http://support.pitneybowes.com/SearchArticles/VFP06_Knowledg...