Big Data Processing Techniques 🛠️
Processing Big Data involves techniques that can scale horizontally across distributed systems. Key methods include batch processing, streaming, and iterative algorithms.
Batch Processing: Processing large volumes of data collected over time. Example: ETL workflows using Hadoop MapReduce.
Streaming Processing: Real-time data analysis as data flows into systems. Tools: Apache Kafka coupled with Spark Streaming.
Iterative Processing: Used in machine learning tasks requiring multiple passes over data. Spark's in-memory processing accelerates these workflows.
Sample Code (Spark Streaming):
from pyspark.streaming import StreamingContext
ssc = StreamingContext(sc, 10) # 10-second batch interval
lines = ssc.socketTextStream('localhost', 9999)
words = lines.flatMap(lambda line: line.split())
words.pairMap(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b).pprint()
ssc.start()
ssc.awaitTermination()