Implementing a Data Lake and Data Ingestion System with CDC Pipeline Using Kafka and Spark

You can create a CDC pipeline using below architecture.

CDC Data Ingestion Pipeline

The overall architecture would consist of the following main components:

1. Source systems: These are the systems that produce the change data, such as MySQL databases with binlogs or Postgres databases with WAL.
2. Apache Kafka: This is the messaging system that is used to collect and transport the change data from the source systems to the data lake.
3. PySpark: This is the data processing engine that is used to process the change data as it is ingested into the data lake. It can be used to filter, transform, and clean the data as needed.
4. Data lake: This is the storage layer for the change data. It can be implemented using Cassandra or AWS S3. It can be used to store the change data in its raw form for later analysis, or it can be used to store a curated version of the data that has been processed and transformed by PySpark.
5. Data lake consumers: This component can be different systems that consume the data from the data lake like Data Warehouses, Data Marts, Business Intelligence systems, machine learning models, reporting systems, and others.
6. Data Governance and Security: This component is responsible for enforcing data governance policies, managing data access, and providing security to the data in the data lake.

A general overview to build an incremental CDC data ingestion pipeline for binlogs and WAL using Apache Kafka, PySpark with Schema Conformance Checks and create datalake over Cassandra or AWS S3 with partition on record creation date

1. Configure Apache Kafka to read binlogs/WALs from the source MySQL/Postgres databases and produce the change data as messages on a specific topic, this can be done by using a Kafka Connector for MySQL like Debezium or Maxwell
2. Use PySpark to consume the messages from the Kafka topic and process the change data. This can include filtering, transforming, and cleaning the data as needed.
3. Perform schema conformance checks using a schema registry in Avro, this can be done by reading the Avro schema from the schema registry and deserializing the Avro data using the schema and comparing it with the schema in the registry.
4. Write the processed data to the data lake, partitioned by record creation date. This can be done by using the PySpark API for writing to Cassandra or S3.
5. Create a PySpark job that runs periodically to check for new changes in the binlogs/WALs and update the data lake accordingly.
6. Implement Data Governance and Security measures: this include access controls, encryption, authentication, and data lineage.

Here is a high-level example of a Python code snippet that demonstrates the main steps involved in building an incremental CDC pipeline for MySQL binlogs using Apache Kafka, PySpark, perform a schema conformance check using a schema registry in Avro, and then dump the data into Cassandra or AWS S3 as partitioned by record creation date:

from pyspark.sql import SparkSession
from pyspark.sql.functions import from_json, col, struct, to_json
from pyspark.sql.types import *
from confluent_kafka import Consumer, KafkaError
from datetime import datetime
from avro.datafile import DataFileReader
from avro.io import DatumReader
from confluent_kafka import avro
from confluent_kafka.avro import AvroProducer

# Start a Spark session
spark = SparkSession.builder.appName("CDC Pipeline").getOrCreate()
# Read the Avro schema from the schema registry
schema_registry_url = 'http://localhost:8081'
reader = avro.reader(schema_registry_url)
schema = reader.schema(topic='avro_topic')
# Define the Kafka consumer
c = Consumer({
    'bootstrap.servers': 'kafka_host:9092',
    'group.id': 'cdc_pipeline',
    'auto.offset.reset': 'earliest'
})
c.subscribe(['binlog_topic'])
# Continuously poll for new messages
while True:
    msg = c.poll(1.0)
    if msg is None:
        continue
    if msg.error():
        if msg.error().code() == KafkaError._PARTITION_EOF:
            print("End of partition event")
        else:
            print("Error occured: {}".format(msg.error()))
    else:
        # Deserialize the Avro data using the schema
        bytes_reader = io.BytesIO(msg.value())
        datum_reader = DatumReader(schema)
        avro_reader = DataFileReader(bytes_reader, datum_reader)
        # Perform a schema conformance check
        for datum in avro_reader:
            if not reader.validate(datum):
                raise ValueError("Data does not conform to schema")
        # Convert the message value to a DataFrame
        data = json.loads(msg.value())
        df = spark.createDataFrame([data])
        
        # Add a new column with the record creation date
        df = df.withColumn("record_creation_date", to_date(col("timestamp_column")))

        # Partition data by record creation date
        partitioned_df = df.repartition(col("record_creation_date"))
        
        # Dump data into AWS S3
        partitioned_df.write.partitionBy("record_creation_date") \
            .format("parquet") \
            .mode("append") \
            .save("s3a://bucket_name/path/to/data")
        
        # Dump data into Cassandra
        partitioned_df.write.format("org.apache.spark.sql.cassandra") \
            .mode("append") \
            .options(table="table_name", keyspace="keyspace_name") \
            .save()

Please note that the above code snippet is a high-level overview of the main steps involved in building a CDC pipeline and would require additional code, configurations and specific integration with the different technologies you’re using, and it is important to consider the specific requirements of your use case when building a pipeline like this.

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