Cloudera CDP Data Analyst case studies
Cloudera CDP Data Analyst · Put your knowledge to work in context.
The Cloudera CDP Data Analyst credential is designed for analysts who work with Cloudera Data Platform (CDP) to query, prepare, and visualize data. It suits professionals who already understand SQL and data analysis concepts and need to demonstrate competence with CDP-specific tools such as Apache Hive, Apache Impala, Apache Kudu, Apache Spark, and Cloudera Data Engineering. The Cloudera CDP Data Analyst case studies are 5 cases and about 4,285 words.
1Broadcast Join After a Regional Backfill
A retail analytics team runs a nightly Impala query that joins a 4.2-billion-row sales fact table (Parquet, partitioned by sale date, 1,460 partitions) to a 90,000-row store dimension. Until last week it finished in about 45 seconds.
868 words, about 4 min2Three Thousand Small CSV Files and a Duplicate Feed
An upstream application drops 22 CSV files per hour into HDFS under /data/events/dt=YYYY-MM-DD/hour=HH - about 3,100 files per day, averaging 4 MB each. Analysts query an external Hive table over that directory using OpenCSVSerde and see a simple GROUP BY event type degrade from 2 minutes to 18 minutes over three months; the NameNode RPC queue length frequently exceeds 200.
868 words, about 4 min3Mutable Alerts: Kudu, HBase, or Parquet
A fraud-monitoring platform must serve two access patterns over the same 250-million-row alert table (growing about 4 million rows per day, retained 24 months): point lookups by account id at 2,000–3,000 requests per second from an application API, and analyst range scans by alert ts with filters on status, run from Impala and a BI tool using standard SQL. The status of each alert changes 3–5 times during investigation, and analysts must see updates within seconds.
879 words, about 4 min4A Straggler Stage in a CDE PySpark Job
A CDE job runs nightly PySpark on a 12-node CDE virtual cluster. It reads 800 million order-line rows from Parquet, joins a 2-million-row product dimension (about 1.2 KB per row), aggregates revenue by customer id, and writes to a Kudu table.
822 words, about 4 min5Ranger Masking Breaks a Join, and a Column Is About to Be Dropped
A bank's CDP cluster governs a customers table with columns customer id, ssn, email, and segment. A Ranger masking policy of type 'redact' (replaces the value with xxxx) is attached to the analyst bi role on ssn, and a row filter restricts those analysts to segment = 'retail'.
848 words, about 4 min