How can you handle schema evolution in cloud-based ETL pipelines?

As you rightly pointed out, backward incompatible schema evolution should be avoided whenever and wherever possible. It leads to more complexity and risks. This is handled well in various low code/cloud platforms. My experience is with MS Azure data factory and here are the steps / approach help me to streamline for one my project (fleet management) where schema keeps changing often, 1. Enable to pipeline to detect change in schema, which minimize the manual intervensio and avoid risk of failing pipeline. 2. Added regorous quality checks in the form of monitoring and creating exception and alert tables to notify for changes so that business knows the change and can consider newly added attributes and add logic to use in your insights.

Handling schema evolution in cloud-based ETL involves several strategies: 1.Dynamic Schema Detection & Inference: Automatically detect and adjust to schema changes at runtime. 2.Schema Evolution Policies: Use flexible schemas, versioning for backward compatibility, and gradual migrations. 3.Data Quality Checks: Implement monitoring, alerts, and validation checks for data integrity post-schema changes. 4.Education & Collaboration: Ensure clear communication and maintain updated documentation on data models and schema changes. By combining these strategies, ETL pipelines can adapt to schema changes efficiently, maintaining data quality and integrity.

Na gest?o da evolu??o de esquemas em pipelines de ETL na nuvem, a flexibilidade é fundamental. Ferramentas de ETL na nuvem devem permitir adapta??es dinamicas aos esquemas em constante mudan?a. Estratégias como detec??o automática de esquemas, mapeamento flexível de dados e monitoramento contínuo garantem a integridade dos dados. Além disso, arquiteturas orientadas a eventos e fluxos de trabalho modulares facilitam a escalabilidade e manuten??o. A documenta??o detalhada dos esquemas e comunica??o eficaz entre equipes s?o essenciais para garantir a consistência dos dados.

One thing that is constant when dealing with systems architecture (yet data architecture) is change. Multiple customers that I talk focus on a short term architecture to solve current challenges and forget about preparing for future growth (scalability, increase in costs, schema evolution, etc..). Some time ago when solutions were based on relational data stores, changes were more manageable. Nowadays, we need to leverage technologies that can evolve with time. Speaking about schema evolution, Iceberg has been an interesting choice comparing to other OTFs because it offers a more comprehensive support for both schema and partition evolution. So picking a specific technology is a important decision for your future!

Schema evolution refers to managing changes in the structure of your data sources, such as databases or files, over time. Banking scenario: Example: Suppose you're developing an ETL process for a banking system. Exsisting Dataset: CustomerID|Name|Address|AccountNumber|Balance Email and Phone is add in ETL process by Bank. New schema looks like this: CustomerID|Name|Address|AccountNumber|Balance|Email|Phone I have used Talend's schema evolution features, you can seamlessly handle this change in the data structure. You might update your Talend job to include the new fields in the data mapping process. Additionally, you can leverage dynamic schema handling to accommodate future changes without modifying the job manually.

Choosing the right data format is pivotal for managing schema evolution in cloud-based ETL pipelines. Opting for schema-based formats like Parquet or Avro can significantly ease the process, as these formats support schema evolution inherently. They enable efficient data storage and retrieval, ensuring compatibility and reducing the overhead of handling schema changes manually. This strategic choice streamlines data processing, enhances performance, and maintains data integrity across evolving datasets.

A evolu??o de esquemas é essencial em ambientes de dados dinamicos. Existem três tipos principais de evolu??o de esquemas: adi??o, altera??o e exclus?o. A adi??o envolve a inclus?o de novos campos ou tabelas para acomodar novos requisitos. A altera??o modifica a estrutura existente, como renomear um campo ou alterar seu tipo de dados. A exclus?o remove campos ou tabelas obsoletos. Cada tipo requer abordagens específicas para garantir a consistência e a integridade dos dados ao longo do tempo.

Data format or file format has an important impact on downstream processing when talking about ETL engines or interactive analytics. As general knowledge, we try to pursuit small number of files with medium to large sizes in order to have a better processing throughput. Not surprisingly, OTFs (that relies on columnar/row-based format like Parquet, Avro) have mechanisms to handle files sizes through compaction processes. An important note tough is that OTFs and columnar file formats are not silver bullets for every data architecture target. Everything depends on the use case and requirements. Besides JSON, CSV and XML are schemaless, they can be easily manipulated with number of tools/frameworks that can deliver results faster and cheaper.

Despite the number of benefits that decoupled architecture bring to us like multiple development teams working separately in different parts of a big application at same time, we still need to have some kind of interoperability between components to be able to release a Megazord to defeat our enemies. A crucial part of this decoupled architecture is a bus/event store/buffer that temporary hold events that are created during application lifetime. The concept of integration bus was used some years ago within service oriented architectures approach to fulfill the glue between different services. A thing to remember tough is that each service has a well defined contract that dictates what service can handle. Same challenge happens for ETL today

For cloud-based ETL schema evolution, using a schema registry effectively manages and tracks schema changes. It ensures compatibility across versions, facilitates smooth data integration, and minimizes disruptions in data processing. Implementing such a centralized system enhances data governance and simplifies schema management across multiple data sources and pipelines.

From client experience - consider ETL visual tools or declarative frameworks that control schema drift. Classic DW isolates drift in your TDS or ODS only. Schema registries can be effective controlling this drift. Two approaches: Visual ETL tool: Dynamic mappings handle schema changes, but scalability and a "handbook plan" must placate data anomalies. Top tier vendors supply schema drift ready templatized no-code pipelines. Declarative frameworks like Databricks DLT: Enable Apache Spark auto-adaption to schema changes, provides visual pipeline, metrics, and data integrity verification. Both methods can complement each other. Adapt the architecture to address data consumer anxiety or costly blockers to strategic goals.

I would consider the below points to handle schema evolution in cloud-based ETL pipelines: - Use schema-on-read tools like Apache Drill, Presto, or Apache Spark that dynamically adapt to schema changes. - Implement versioning with tools such as Schema Registry to manage schema evolution. - Leverage ETL tools with schema evolution support, such as Apache NiFi, Talend, or AWS Glue. - Automate schema detection with scripts in Python or Scala that utilize Spark's DataFrame API. - Utilize data formats like Avro, JSON, or Parquet for flexible schema on storage. - Employ logging with ELK Stack (Elasticsearch, Logstash, Kibana) or Amazon CloudWatch for monitoring schema changes.

An alternative option is design a ETL pipeline based in syntacic focus: store fields of data like independent records with its associated metadata to store it in dedicated space; and for new fields in data, create a new space to store it. You can use metadata management and data quality to create an unified and standard version of data to load in business storage component. This way to avoid directly manage the schema evolution: check if it works for you.

For schema evolution in cloud-based ETL, consider the potential for automation in handling schema changes. Tools that automatically adapt to schema modifications can reduce manual oversight and errors, enhancing efficiency and reliability.