In today’s rapidly evolving digital landscape, businesses are generating unprecedented volumes of data at increasing velocities. This deluge of information holds immense potential for driving strategic decisions, optimizing operations, and enhancing customer experiences. However, traditional data architectures often struggle to effectively handle this complexity. Data Lakes, while offering scalability and flexibility for storing diverse data types, typically lack the robust governance and transactional consistency required for reliable analytics. Conversely, Data Warehouses provide structured environments optimized for business intelligence but can be rigid and expensive when dealing with large, semi-structured, or streaming data.
This dichotomy has often forced organizations to maintain separate and often siloed data platforms, leading to data duplication, hindered data governance, increased complexity and delays in accessing timely insights.
The emergence of the Data Lakehouse architecture represents a paradigm shift, aiming to unify the best of both worlds. By introducing a transactional and governance layer to the data lake, it enables reliable analytics directly on vast amounts of raw and processed data. This eliminates the need for complex ETL (Extract, Transform, Load) pipelines to move data between systems, paving the way for more agile and efficient data-driven workflows. But how can organizations practically implement a Data Lakehouse to unlock the power of real-time analytics?
This blog post delves into the practical aspects of building such an architecture, focusing on key components and leveraging the capabilities of platforms like Databricks to achieve low-latency insights and drive immediate business value. We will explore the core principles, architectural considerations, and practical implementation strategies that empower businesses to move beyond batch processing and embrace the agility of real-time data analysis.
The Evolution Towards Real-Time Data Lakehouses
The journey from traditional Data Warehouses to Data Lakes and now to the Data Lakehouse has been driven by the increasing demand for faster insights from diverse datasets.
Limitations of Traditional Data Architectures for Real-Time
Traditional Data Warehouses, with their rigid schemas and batch-oriented processing, are inherently challenged when it comes to real-time analytics. Ingesting and transforming streaming data into a data warehouse with low latency can be complex and resource-intensive, and in most cases could be scaled up only on single machines, and in most cases could be scaled up only on single machines. On the other hand, data lakes excel at ingesting high-velocity and highly variable data (structured, semistructured, unstructured), but often lack the ACID (Atomicity, Consistency, Isolation, Durability) properties and robust governance frameworks necessary for reliable, consistent, and timely analytical queries. This often results in a “data swamp” where extracting meaningful insights quickly becomes a significant hurdle.
The Promise of the Data Lakehouse for Low-Latency Insights
The Data Lakehouse architecture addresses these limitations by introducing a metadata layer and a storage layer that supports transactions and data governance directly on the data lake. This enables:
- Direct Querying of Real-Time Data: With technologies like Delta Lake and Apache Iceberg, streaming data can be ingested and immediately made available for querying alongside batch data.
- Simplified Data Pipelines: The need for extensive ETL processes to move data to a separate analytical system is significantly reduced, leading to lower latency and complexity.
- Unified Data Governance: Consistent security, compliance, and data quality controls can be applied across all data, regardless of its format or processing stage.
- Callout: According to a recent report by Gartner, organizations adopting a data lakehouse architecture are 3x more likely to achieve real-time analytics capabilities compared to those relying solely on traditional data warehouses.
Real-Time Analytics in a Data Lakehouse
Several key technologies and architectural patterns enable real-time analytics within a Data Lakehouse:
- Streaming Data Ingestion: Frameworks like Apache Kafka, Apache Flink, and cloud-native streaming services (e.g., AWS Kinesis, Azure Event Hubs) facilitate the continuous ingestion of high-velocity data.
- Transactional Data Lake Storage: Formats like Delta Lake and Apache Iceberg provide ACID properties and schema evolution capabilities on top of Data Lake storage (e.g., AWS S3, Azure Data Lake Storage). This ensures data consistency and reliability for real-time queries.
- Real-Time Processing Engines: Compute engines like Apache Spark Structured Streaming and Flink allow for continuous processing and analysis of streaming data, enabling the creation of real-time dashboards and alerts.
- Low-Latency Query Engines: Optimized query engines can directly query the transactional Data Lake, providing sub-second response times for analytical workloads on both batch and streaming data.
Building a Practical Real-Time Data Lakehouse with Databricks
Databricks is a unified analytics platform that provides a robust environment for building and operating a Data Lakehouse, with strong support for real-time analytics.
High-level architecture example of real-time data flow in the Databricks Lakehouse from ingestion through Autoloader, Spark Structured Streaming and Kafka/AWS Kinesis to BI applications (Databricks SQL, Dashboards) and AI applications (ML model training/serving, GenAI, Genie). Please note that this example aArcssumes AWS as cloud provider for the Databricks platform: however, the architecture shown can be deployed also on Microsoft Azure and Google Cloud Platform. Figure adapted from https://docs.databricks.com/aws/en/lakehouse-architecture/reference.
Leveraging Delta Lake for Real-Time Data Ingestion and Processing
Delta Lake – an open-source storage layer that extends Parquet data files – is a cornerstone of building a real-time Data Lakehouse on Databricks.
Its key features that enable real-time capabilities include:
- ACID Transactions: Ensure data integrity even during concurrent read and write operations, crucial for handling continuous data streams.
- Schema Evolution: Allows for seamless changes to the data schema without disrupting downstream applications or requiring complex migrations.
- Time Travel: Enables querying historical data snapshots, which can be valuable for analyzing trends and understanding the evolution of real-time data.
- Unified Batch and Streaming Source/Sink: Delta Lake tables can serve as both a source for batch processing and a sink for streaming data, simplifying data pipelines.
Code Example: Ingesting a real-time stream into a Delta Lake table using Spark Structured Streaming on Databricks (Python):
from pyspark.sql import SparkSession
from pyspark.sql.functions import from_json, col
from pyspark.sql.types import StructType, StringType, IntegerType, TimestampType
# Define the schema for the incoming JSON data
schema = StructType([
StructType([
StringType("device_id"),
TimestampType("timestamp"),
IntegerType("temperature"),
StringType("location")
])
])
# Configure the Spark Session
spark = SparkSession.builder.appName("RealTimeDataIngestion").getOrCreate()
# Read the streaming data from a Kafka topic
streaming_df = spark.readStream.format("kafka") \
.option("kafka.bootstrap.servers", "your_kafka_brokers") \
.option("subscribe", "sensor_data_topic") \
.load() \
.select(from_json(col("value").cast("string"), schema).alias("data")) \
.select("data.*")
# Write the streaming data to a Delta Lake table
query = streaming_df.writeStream \
.format("delta") \
.outputMode("append") \
.option("checkpointLocation", "/mnt/delta/checkpoints/sensor_data") \
.start("/mnt/delta/sensor_data")
query.awaitTermination()
Commentary: This code snippet demonstrates how to read a real-time data stream from a Kafka topic, define the schema of the incoming JSON data, and then continuously append this data to a Delta Lake table.
Real-Time Processing with Spark Structured Streaming on Databricks
Databricks provides a powerful environment for leveraging Apache Spark Structured Streaming for real-time data processing and analysis on data stored in Delta Lake. Structured Streaming allows you to build scalable and fault-tolerant streaming applications using the same syntax of batch computation. You can perform complex transformations, aggregations, and windowing operations on streaming data with low latency. Real-time streaming in Databricks can also harness the power of Delta Live Tables and Autoloader by combining incremental file ingestion with declarative, production-grade ETL pipelines that simplify data engineering and accelerate time to insight.
Code Example: Performing real-time aggregations on sensor data ingested into Delta Lake (Python):
from pyspark.sql import SparkSession
from pyspark.sql.functions import avg, window
# Configure the Spark Session
spark = SparkSession.builder.appName("RealTimeAnalytics").getOrCreate()
# Read the Delta Lake table as a stream
streaming_df = spark.readStream.format("delta") \
.load("/mnt/delta/sensor_data")
# Perform real-time aggregation to calculate the average temperature per device in a 5-minute window
aggregated_df = streaming_df \
.groupBy(window("timestamp", "5 minutes"), "device_id") \
.agg(avg("temperature").alias("avg_temperature"))
# Write the results to another Delta Lake table or a real-time dashboard sink
query = aggregated_df.writeStream \
.format("delta") \
.outputMode("complete") \
.option("checkpointLocation", "/mnt/delta/checkpoints/avg_temperature") \
.start("/mnt/delta/avg_temperature_realtime")
query.awaitTermination()
Commentary: This example reads the sensor_data Delta Lake table as a stream and then performs a real-time aggregation to calculate the average temperature for each device within a 5-minute sliding window. The results are then written to another Delta Lake table, which could be used to power real-time dashboards or trigger alerts.
Serving Real-Time Insights with Databricks SQL Analytics
Databricks SQL Analytics provides a serverless SQL data warehouse optimized for data lakehouses. It allows business analysts and data scientists to run fast, interactive SQL queries directly on the Delta Lake tables, including those being continuously updated by streaming pipelines. This enables low-latency access to real-time insights without the need for separate data marts or cubes.
Enabling Real-Time ML Training and Inference
In addition to powering real-time analytics, the Data Lakehouse architecture offers significant advantages for end-to-end AI and machine learning (ML) workflows — from feature engineering to model training, management, and serving.
The Lakehouse eliminates the traditional friction between analytics and ML systems by unifying structured, semi-structured, and streaming data in one ACID-compliant storage layer. Data scientists and ML engineers can access both real-time and historical data directly from Delta Lake tables, with the groundbreaking opportunity of leveraging fresh data for model training and inference.
Databricks provides native support for ML development within the Lakehouse, allowing teams to collaborate across the entire ML lifecycle. Feature engineering can be done in-place using batch and streaming sources; training pipelines can scale seamlessly with Spark or MLflow; and models can be versioned, registered, and deployed directly on the Lakehouse using Model Serving.
Practical Considerations for Real-Time Lakehouse Implementation
Building a successful real-time Data Lakehouse requires careful consideration of several practical aspects:
Data Governance and Quality in Real-Time Pipelines
Maintaining data quality and governance in real-time pipelines is crucial. Implementing data validation checks, schema enforcement, and data lineage tracking early in the ingestion process is essential to ensure the reliability of real-time analytics. Delta Lake’s schema evolution and ACID properties contribute significantly to data quality.
Scalability and Performance Optimization
Real-time data pipelines often need to handle high volumes of data with low latency. Choosing scalable ingestion and processing frameworks, optimizing query performance on the data lake, and leveraging cloud-native auto-scaling capabilities are critical for ensuring the system can handle fluctuating workloads. Databricks’ auto-scaling clusters and optimized SQL engine help address these challenges.
Monitoring and Alerting for Real-Time Systems
Robust monitoring and alerting are essential for maintaining the health and performance of real-time data lakehouse deployments. Tracking key metrics like ingestion latency, processing throughput, and query response times allows for proactive identification and resolution of potential issues. Databricks provides built-in monitoring tools and integrates with other monitoring solutions.
Why Choose Bitrock for Your Data Lakehouse Journey?
At Bitrock we help organizations design, build, and deploy modern data architectures, including practical Data Lakehouse solutions tailored to their specific business needs. Our team of experienced data engineers and architects harbours an outstanding understanding of technologies like Databricks, Delta Lake, Apache Spark, and various cloud platforms.
We offer an end-to-end integrated approach to guide you through every stage of your data transformation journey:
- Strategic Consulting: We work closely with your business and IT stakeholders to define your real-time analytics requirements and design a future-proof Data Lakehouse architecture that aligns with your strategic objectives.
- Implementation and Deployment: Our expert team implements robust and scalable real-time data pipelines and lakehouse environments on your chosen platform, ensuring seamless integration with your existing systems.
- Optimization and Governance: We help you optimize the performance of your real-time analytics workloads and establish effective data governance frameworks to ensure data quality, security, and compliance.
- Ongoing Support and Maintenance: We provide continuous support and maintenance services to ensure the reliability and optimal performance of your Data Lakehouse environment.
Conclusions
The practical implementation of a Data Lakehouse architecture, particularly when leveraging platforms like Databricks and technologies like Delta Lake, offers a compelling path towards achieving real-time analytics capabilities.
By unifying data storage and processing, simplifying pipelines, and enabling low-latency querying, organizations can unlock immediate insights from their data streams, driving faster and more informed decisions. While careful planning, robust governance, and continuous optimization are essential, the benefits of real-time data-driven decision-making are undeniable.
Are you ready to move beyond batch processing and embrace the power of real-time insights with a practical Data Lakehouse? Contact us today to explore how we can help you embark on this transformative journey.
Main Author: Domenico Simone, Data Engineer @ Bitrock
