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What are the most effective alternatives to batch processing?

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Batch processing is a common technique in data engineering that involves executing a series of tasks on a large set of data at regular intervals. Batch processing is often used for tasks such as data ingestion, transformation, aggregation, analysis, and reporting. However, batch processing also has some limitations, such as high latency, low scalability, and complex dependencies. In this article, you will learn about some of the most effective alternatives to batch processing that can help you overcome these challenges and improve your data engineering workflows.

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Youssef Mrini

Data & AI Architect | NextGenLakehouse | Opinions are my own.
Chandramohan Pounrajan

AWS | Azure | API | Design Patterns | .NET | Cloud Migration | Digital Transformations | Kafka | Application Scaling |…
Akshay Vijay

Senior Data Engineer | Building Scalable Data Pipelines for Fortune 500 Companies | 5+ Years | DataOps | H1B Approved |…

1 Stream processing

Stream processing is a technique that involves processing data as soon as it arrives, rather than waiting for a batch window. Stream processing can handle high-volume and high-velocity data sources, such as sensors, web logs, or social media feeds. Stream processing can provide low-latency, real-time insights, and enable event-driven applications. Stream processing requires a different architecture and toolset than batch processing, such as Apache Kafka, Apache Spark Streaming, or Apache Flink.

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Alestan Alves

Data Engineering Coordinator at TOTVS | Creator at @ackercode 300k+ followers
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Stream processing allows real-time data analysis immediately upon arrival, bypassing the need for batch intervals. It excels with rapid, large data sources like sensors, web logs, and social feeds, offering swift insights and supporting reactive applications. This method demands unique architectures and tools, including Apache Kafka, Apache Spark Streaming, and Apache Flink, to efficiently process and harness the flow of data. Ideal for environments requiring instant decision-making, stream processing enables businesses and technologies to respond dynamically to live data streams.

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2 Micro-batch processing

Micro-batch processing is a technique that involves processing data in small batches that are generated at short intervals, such as seconds or minutes. Micro-batch processing can achieve a balance between latency and throughput, and leverage some of the advantages of both batch and stream processing. Micro-batch processing can handle data sources that are not strictly ordered or continuous, such as files or databases. Micro-batch processing can use some of the same tools as batch processing, such as Apache Spark or Apache Airflow, with some modifications.

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Chandramohan Pounrajan

AWS | Azure | API | Design Patterns | .NET | Cloud Migration | Digital Transformations | Kafka | Application Scaling | Data Brokers |
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This could be a one of the good approaches or techniques. This will ensure data quality, data integrity, reduce in computing power, near real-time data availability, etc

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3 Lambda architecture

Lambda architecture is a design pattern that involves combining batch and stream processing in a single system. Lambda architecture consists of three layers: the batch layer, the speed layer, and the serving layer. The batch layer handles the historical data and performs complex and accurate computations. The speed layer handles the recent data and performs fast and approximate computations. The serving layer merges the results from both layers and provides a unified view of the data. Lambda architecture can handle both latency and accuracy requirements, but also introduces complexity and redundancy.

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Alestan Alves

Data Engineering Coordinator at TOTVS | Creator at @ackercode 300k+ followers
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Advantages of Lambda Architectures: - Serverless Operations: Eliminates the need for installing, maintaining, or managing software. - Scalability: Allows applications to scale flexibly, either automatically or through capacity adjustments. - Built-in Reliability: Serverless applications come with inherent high availability and fault tolerance, ensuring all requests receive feedback on their success. - Enhanced Business Flexibility: Enables immediate response to shifts in business or market conditions. Serveless Tools I like: AWS Lambda & Cloud Function GCP

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4 Kappa architecture

Kappa architecture is a design pattern that involves using only stream processing in a single system. Kappa architecture consists of two layers: the stream layer and the serving layer. The stream layer processes all the data as it arrives, and applies the same logic to both historical and recent data. The serving layer stores and queries the results from the stream layer. Kappa architecture can simplify the system and reduce the maintenance overhead, but also requires a reliable and scalable stream processing framework.

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5 Delta architecture

Delta architecture is a design pattern that involves using a unified data storage layer that supports both batch and stream processing. Delta architecture consists of three layers: the bronze layer, the silver layer, and the gold layer. The bronze layer stores the raw data as it arrives, without any transformation or validation. The silver layer applies quality checks, deduplication, and schema enforcement to the data, and creates a consistent and reliable data lake. The gold layer performs aggregations, joins, and business logic to the data, and creates a curated and optimized data warehouse. Delta architecture can enable both batch and stream processing to access the same data source, and provide a single source of truth for the data.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Youssef Mrini

Data & AI Architect | NextGenLakehouse | Opinions are my own.
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I would recommend to try Databricks for those various options. It's gonna be a suitable solution for Streaming and Batch processing. When I'm talking about Streaming it doesn't mean you need to have a cluster/ compute always on, it really depends on the SLA your business is trying to achieve. It can also be a managed ETL with Delta live tables which can help you reduce drastically the cost and increase the efficiency.

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Akshay Vijay

Senior Data Engineer | Building Scalable Data Pipelines for Fortune 500 Companies | 5+ Years | DataOps | H1B Approved | 3X Databricks Certified Data Engineer | Microsoft Azure Certified Professional
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Some effective alternatives to batch processing include real-time processing, stream processing, and event-driven processing. These approaches allow for faster data processing and analysis by handling data as it arrives, rather than waiting for a batch to accumulate before processing.

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What are the most effective alternatives to batch processing?

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6

1 Stream processing

2 Micro-batch processing

3 Lambda architecture

4 Kappa architecture

5 Delta architecture

6 Here’s what else to consider

Data Engineering

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What are the most effective alternatives to batch processing?

1

2

3

4

5

6

1 Stream processing

2 Micro-batch processing

3 Lambda architecture

4 Kappa architecture

5 Delta architecture

6 Here’s what else to consider

Data Engineering

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