Examius
🌍 Data Science · flashcards
Data Science Databases Flashcards
50 question-and-answer cards covering Databases as it is examined in Data Science. 24 of them are printed below, taken from across the deck — no signup, no paywall on the preview.
24 sample cards from the Databases deck
Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.
What is eventual consistency?
A consistency model in which, given no new updates, all replicas will converge to the same value over time, though reads may temporarily return stale data.
Name the four major NoSQL data-store categories.
Document-oriented, Key-Value, Column-Family (wide-column), and Graph databases.
What is a document-oriented database and what data format does it typically use?
A NoSQL store that holds data as self-describing documents (typically JSON, BSON or XML), where each document is a flexible, nested key-value structure. Example: MongoDB. Documents are grouped in collections.
What is a key-value store and what is its main strength?
The simplest NoSQL model, storing data as unique key to opaque value pairs. Its strength is extremely fast $O(1)$ lookups, inserts and deletes by key. Examples: Redis, DynamoDB, Riak.
What is a column-family (wide-column) store?
A NoSQL model that stores data in rows grouped into column families, where columns are stored together on disk and rows can have different columns. Optimized for fast reads/writes over huge sparse datasets. Examples: Cassandra, HBase.
Why does column-oriented storage speed up analytical queries?
Because values of a single column are stored contiguously, analytical scans read only the needed columns (less I/O) and achieve better compression since similar values are adjacent.
What is a graph database and what two primitives does it model?
A NoSQL database that stores data as nodes (entities) and edges (relationships), often with properties on both. It excels at traversing connected data. Examples: Neo4j, Amazon Neptune.
When are graph databases preferable to relational databases?
When relationships are first-class and queries involve deep or variable-length traversals (social networks, recommendation engines, fraud detection), avoiding expensive repeated joins.
Define Big Data using the classic "3 Vs".
Volume (huge amounts of data), Velocity (high speed of generation/processing), and Variety (structured, semi-structured and unstructured formats). Often extended with Veracity (trustworthiness) and Value.
What two core components form the foundation of Apache Hadoop?
HDFS (Hadoop Distributed File System) for distributed storage, and MapReduce (with YARN for resource management) for distributed processing.
What are the two phases of the MapReduce programming model?
The Map phase transforms input into intermediate key-value pairs; the Reduce phase aggregates all values sharing a key. A shuffle-and-sort step groups intermediate pairs by key between them.
In HDFS, what is the role of the NameNode versus the DataNode?
The NameNode is the master that stores filesystem metadata (the namespace and block locations); DataNodes are workers that store the actual data blocks and serve read/write requests.
What is the default replication factor in HDFS and why is it used?
The default replication factor is $3$: each block is stored on three nodes to provide fault tolerance and data availability if a node fails.
What is YARN's role in the Hadoop ecosystem?
YARN (Yet Another Resource Negotiator) is Hadoop's cluster resource manager and job scheduler, separating resource management from processing so multiple engines (MapReduce, Spark) can share the cluster.
What is Apache Spark and how does its core data structure differ from MapReduce?
Spark is a distributed in-memory processing engine. Its core abstraction is the RDD (Resilient Distributed Dataset), an immutable, partitioned, fault-tolerant collection. By keeping data in memory it is far faster than disk-based MapReduce for iterative work.
Differentiate transformations from actions in Spark.
Transformations (e.g. map, filter, join) are lazy and build a lineage of a new RDD/DataFrame without computing. Actions (e.g. collect, count, save) trigger actual execution of the lineage and return a result.
How does Spark achieve fault tolerance for RDDs?
Through lineage: each RDD records the sequence of transformations that built it, so lost partitions can be recomputed from the source rather than relying solely on replication.
What is data partitioning (sharding) in a distributed database?
Splitting a dataset horizontally across multiple nodes by a shard/partition key so each node holds a subset, enabling horizontal scaling of storage and load.
Distinguish replication from partitioning (sharding).
Replication copies the same data to multiple nodes for availability and read scaling/fault tolerance; partitioning (sharding) splits different data across nodes for write scaling and capacity. They are often combined.
What is a data warehouse and how does it differ from an operational (OLTP) database?
A data warehouse is a subject-oriented, integrated, time-variant, non-volatile repository optimized for analytical queries (OLAP) over historical data. OLTP databases are optimized for fast, frequent, current-state read/write transactions.
Contrast OLTP with OLAP.
OLTP (Online Transaction Processing): many short read/write transactions on current data, normalized schema. OLAP (Online Analytical Processing): complex read-heavy aggregations over large historical datasets, denormalized/star schema.
What are the three steps of ETL?
Extract data from source systems, Transform it (clean, validate, deduplicate, aggregate, reformat to the target schema), and Load it into the target data warehouse.
How does ELT differ from ETL and why has it become popular?
In ELT, raw data is Extracted and Loaded into the target first, then Transformed using the warehouse's own compute. It suits cloud warehouses and data lakes with cheap, scalable storage and processing, and preserves raw data for flexible later transformation.
Contrast a star schema with a snowflake schema in data warehouse design.
A star schema has a central fact table linked to denormalized dimension tables (simple, fast queries). A snowflake schema normalizes the dimensions into related sub-tables (less redundancy, more joins, slightly slower queries).
What this deck covers
The Databases deck follows the Data Science Databases syllabus — 7 chapters and 25 topics — so questions land on material that is genuinely examinable rather than trivia around it. That works out to roughly 7.1 cards per chapter.
Answers are written to be recallable, not just readable — averaging about 192 characters, which is long enough to carry the reasoning and short enough to say out loud.
A deck like this earns its keep on the second and third pass. Read the syllabus first so you know the shape of the subject, then use the cards to find the specific facts that have not stuck.
Databases flashcards FAQ
How many Databases flashcards are in this Data Science deck?
50 cards. This page previews 24 of them, sampled evenly across the deck so you can judge the difficulty before installing anything.
Are these Data Science flashcards free?
Yes. The preview here is free to read with no signup, and the full 50-card deck is free inside the Examius app.
What do the Databases cards cover?
They follow the Data Science Databases syllabus — 7 chapters and 25 topics — so the questions track what is actually examinable.
How should I use these flashcards?
Read the syllabus first so you know the shape of the subject, then drill the deck. Examius schedules each card with spaced repetition, so cards you keep missing come back sooner and ones you know drift further apart.