Bridging the Gap: Unifying Data Lakes and Warehouses with Real-Time Operational Data
Bridging the Gap: Unifying Data Lakes and Warehouses with Real-Time Operational Data
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Bridging the Gap: Unifying Data Lakes and Warehouses with Real-Time Operational Data
Unifying Data Lakes and Warehouses with Operational Data: The Role of Tableflow
Data lakes and warehouses have long served as the foundation for modern data infrastructure. While data lakes enable scalable, raw data storage, data warehouses provide structured analytics. Yet, they often struggle with real-time operational data, leading to fragmented insights and delayed decision-making.
Making Kafka Data Usable for AI & Analytics: Overcoming the Challenges of Streaming Data Integration
For businesses relying on Apache Kafka to capture real-time events, the value of streaming data is undeniable. It fuels faster decision-making, powers AI models, and enables deeper insights. But here’s the problem—getting Kafka data into a data lake or warehouse for structured analytics is a nightmare.
Why Kafka Data Isn’t Ready for Analytics Out of the Box
Kafka was built for high-throughput event streaming, not for structured table queries. This means data engineers face several roadblocks when trying to use streaming data for analytics and AI:
🚧 Messy, Unstructured Data – Kafka events arrive in formats like Avro, JSON, or Protobuf, while data lakes prefer Parquet for efficient querying.
⚠️ Schema Evolution Issues – Data constantly evolves, causing schema mismatches that can break pipelines. Engineers need custom logic to handle changes.
💾 Compaction & File Management Problems – Streaming data creates thousands of tiny files, slowing down query performance. Without compaction, retrieval is inefficient and expensive.
🔄 Fragile & Costly Pipelines – To ingest Kafka data into Apache Iceberg or Delta Lake, engineers must manually stitch together complex ETL processes—each requiring ongoing maintenance.
Tableflow: A Better Way to Integrate Kafka with Data Lakes
Instead of dealing with fragile pipelines, Tableflow provides an automated solution. With Tableflow, any Kafka topic with a schema can be exposed as a Delta Lake or Iceberg table—no manual conversion, no extra cleanup, no duplicated data.
How Tableflow Works
✅ Instantly converts Kafka streams into structured tables
✅ Removes the need for custom ETL jobs
✅ Ensures schema evolution is handled seamlessly
✅ Optimizes file management for fast queries
Tableflow is a game-changer for organizations struggling with data engineering bottlenecks. By eliminating the complexities of ingesting Kafka data into queryable tables, businesses can unlock real-time analytics, AI-driven insights, and smarter decision-making—without the usual headaches.
How Tableflow Seamlessly Transforms Kafka Streams into Structured Tables
Managing Kafka data for analytics has traditionally been complex and resource-intensive. But Tableflow simplifies the process by seamlessly converting Kafka segments into Delta Lake and Apache Iceberg tables—eliminating tedious manual transformations and making real-time data instantly accessible for AI and analytics.
What’s Happening Behind the Scenes?
Tableflow leverages Kora Storage Layer innovations to take Kafka segments and efficiently store them as Parquet files—the preferred format for analytics-ready data lakes. It also taps into Confluent’s Schema Registry, ensuring that metadata, schema mapping, evolution, and type conversions are all handled automatically.
Key Features That Make Tableflow Game-Changing
🔄 Smart Data Conversion: Transforms Kafka data from formats like Avro, JSON, or Protobuf into Iceberg- and Delta-compatible schemas without requiring manual intervention.
📌 Schema Evolution Made Simple: Detects and applies changes—like adding fields or adjusting data types—so tables remain up-to-date without breaking pipelines.
🔗 Seamless Catalog Syncing: Easily integrates Tableflow-generated tables with AWS Glue, Snowflake Open Catalog, Apache Polaris, and soon, Unity Catalog—ensuring compatibility across ecosystems.
🛠️ Automated Table Maintenance: Tableflow compacts small files to optimize query performance and manages snapshot expiration, reducing storage overhead.
☁️ Flexible Storage Options: Users can store their data in their own Amazon S3 bucket or opt for fully managed storage via Confluent.
The End of Manual Data Preprocessing
Tableflow eliminates the hassle of manually cleaning, converting, and maintaining Kafka data streams. By instantly representing raw streaming data as high-quality Iceberg and Delta tables, it ensures analytics teams can focus on insights rather than infrastructure.
No more fragile pipelines, schema headaches, or performance bottlenecks—just structured, optimized, and query-ready data, exactly where you need it.
The Future of Real-Time Data Infrastructure
With Tableflow now generally available on AWS, it’s clear that the era of manual, fragile pipelines is coming to an end. Businesses can finally shift from managing streams manually to working with structured, analytics-ready data effortlessly.
It’s time to rethink how we handle operational data in data lakes and warehouses. The future isn’t just streaming—it’s structured, automated, and open.
Real-World Use Case: Real-Time User Activity Tracking
Company Example: A platform like Netflix, Disney+, or LinkedIn Scenario:
- Ingest clickstream events (e.g., "user watched video X") from Kafka.
- Write directly to an Iceberg table for real-time analytics (e.g., trending videos).
- Use Iceberg’s ACID compliance and time travel for reliability.
Tech Stack
ComponentRoleApache KafkaSource of streaming events (e.g., user_clicks topic).PySparkProcesses Kafka streams and writes to Iceberg.Apache IcebergSink table format (supports upserts, schema evolution, time travel).
Step-by-Step PySpark Code
1. Set Up Spark Session with Iceberg
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("KafkaToIceberg") \
.config("spark.sql.catalog.iceberg_catalog", "org.apache.iceberg.spark.SparkCatalog") \
.config("spark.sql.catalog.iceberg_catalog.type", "hadoop") \
.config("spark.sql.catalog.iceberg_catalog.warehouse", "s3://iceberg-warehouse") \
.config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
.getOrCreate()2. Read from Kafka Topic
# Define Kafka source
df_kafka = spark.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "kafka-broker:9092") \
.option("subscribe", "user_clicks") \
.option("startingOffsets", "latest") \
.load()
# Parse JSON events (assuming Kafka messages are JSON)
from pyspark.sql.functions import from_json, col
schema = "user_id STRING, movie_id STRING, timestamp TIMESTAMP"
df_parsed = df_kafka.select(
from_json(col("value").cast("string"), schema).alias("data")
).select("data.*")3. Transform Data (Optional)
# Example: Filter valid events and add a processing time column
from pyspark.sql.functions import current_timestamp
df_transformed = df_parsed.filter("movie_id IS NOT NULL") \
.withColumn("processing_time", current_timestamp())4. Write to Iceberg Table
# Write to Iceberg in streaming mode
query = df_transformed.writeStream \
.format("iceberg") \
.outputMode("append") \
.option("path", "iceberg_catalog.analytics.user_clicks") \
.option("checkpointLocation", "s3://checkpoints/kafka-iceberg") \
.trigger(processingTime="1 minute") \ # Micro-batch every 1 minute
.start()
query.awaitTermination()Key Iceberg Features Used
- ACID Transactions:
- Time Travel:
- Schema Evolution:
Performance Optimizations
Performance Optimizations
- Partitioning:
- Queries only scan relevant partitions (e.g., WHERE date(timestamp) = '2024-05-20').
df_transformed.writeTo("iceberg_catalog.analytics.user_clicks") \
.partitionedBy("date(timestamp)") \ # Partition by day
.createOrReplace()Merge-on-Read (Upserts):
# Requires Iceberg v1+ and Spark 3.5+
df_transformed.writeStream \
.format("iceberg") \
.option("mergeSchema", "true") \
.option("write.upsert.enabled", "true") \
.start("iceberg_catalog.analytics.user_clicks")Real-World Companies Using This Pattern
- Netflix-
Kafka (user watch events) → Spark Streaming → Iceberg (real-time recommendations).
- LinkedIn-Kafka (engagement events) → Iceberg (analytics dashboards).
- Walmart - Kafka (inventory updates) → Iceberg (supply chain monitoring).
Summary
- Direct Kafka → Iceberg writes are possible with PySpark Structured Streaming.
- Iceberg provides transactional guarantees and analytics-friendly features.
- Used by Netflix, LinkedIn, and Walmart for real-time data pipelines.
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