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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to Read Kafka Logs Quickly

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In Kafka, the log file's function is to store entries. Here, you can find entries for the producer's incoming messages. You can call these topics. And, topics are divided into partitions.


How to Read Logs in Kafka

IN THIS PAGE

  1. Kafka Logs
  2. How Producer Messages Store
  3. Benefits of Kafka Logs
  4. How to check Logs in Kafka
How to Read Kafka Logs Quickly

1. Kafka Logs

  • The mechanism underlying Kafka is the log. Most software engineers are familiar with this. It tracks what an application is doing. 
  • If you have performance issues or errors in your application, the first place to check is the application logs. But it is a different sort of log. 
  • In the context of Kafka (or any other distributed system), a log is "an append-only, totally ordered sequence of records - ordered by time.

Kafka Basics [Video]





2. How Producer Messages Store

  • The producer writes the messages to Broker, and the records are stored in a log file. The records are stored as 0,1,2,3 and so on.
  • Each record will have one unique id.

4. Benefits of Kafka Logs

  • Logs are a simple data abstraction with powerful implications. If you have records in order with time, resolving conflicts, or determining which update to apply to different machines becomes straightforward.
  • Topics in Kafka are logs that are segregated by topic name. You could almost think of topics as labeled logs. If the log is replicated among a cluster of machines, and a single machine goes down, it’s easy to bring that server back up: just replay the log file. 
  • The ability to recover from failure is precisely the role of a distributed commit log.

5. How to Read Logs in Kafka

# The directory under which to store log files 

$  log.dir=/tmp/kafka8-logs 

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