How can you make your data pipelines accessible to different applications?

In my view, to make data pipelines accessible, a few steps should be considered, such as: Consider using industry standard protocols, Consider using standard data formats (JSON, CSV or Parquet) to ensure compatibility with a wide variety of applications, Provide clear and comprehensive documentation for your APIs to guide developers on how to interact with their data, Implement robust authentication mechanisms to control access to your data pipelines, Maintain a data catalog that describes the available datasets, their schemas and usage guidelines and Implement robust monitoring and logging mechanisms to monitor the performance and health of your data pipelines.

You can achieve this with use standard formats and protocols fixed length text file, JSON, XML, CSV, Parquet for structured or semi-structured data. Use SFTP, API (Webservices, SOAP), Kafka producer-consumer, SAMBA, Direct extraction for data transfer. Do you know what we forget to give attention to? ENCODING to UTF-8. The files takes encoding of native OS where those are written if no enforced and that can cause direct impact on its readability on downstream systems.

Standard formats like JSON, CSV, or Parquet streamline data exchange. Protocols like HTTP, FTP, or Kafka facilitate seamless data transfer. Widely supported formats and protocols minimize integration complexities. Use of standard formats and protocols enhances accessibility across applications.

Parte de los protocolos es la forma en como gestionas la entrega de datos: si son muchos datos a entregar, hay que indicar las formas de entrega según el mecanismo y el formato: 1) API; GraphQL o REST; las paginación es un gran opción para formatos JSON o CSV 2) Por medio de referencia a archivos temporales; enviar URL para descargar archivos. También usar servidores SFTP para entregar datos es crucial. 3) En caso de flujos de datos, usar RESThook, Webhook or integrarse a un mecanimos publicador-consumidor como kafka ayudan un buen.

Exposing data through API ( Eg Open ai) and Micro Services is the new and easy way of data pipeline accessible for modern data platform architecture.

APIs provide a standardized interface for accessing and manipulating data, regardless of the underlying implementation or technology stack. This standardization simplifies the integration process and promotes consistency across different applications and environments.

APIs facilitate communication with data pipelines. Documentation explains API usage: parameters, responses, errors, examples. APIs and documentation aid developers in understanding and interacting with data pipelines. Accessing data pipelines through APIs enables querying, filtering, updating, and deleting data. Providing APIs and documentation enhances accessibility and usability of data pipelines.

These methods, including SSL/TLS, OAuth, JWT, and API keys, are utilized to implement security and authentication in data pipelines. SSL/TLS encrypts data transmission to ensure confidentiality and integrity, while OAuth enables secure access without sharing user credentials. JWT facilitates stateless authentication, and API keys provide unique identifiers for accessing protected resources, collectively ensuring secure and authorized data access within data pipelines.

El monitoreo ayudara a controlar el uso la canalización de datos por parte de las aplicaciones consumidoras en diferentes formas: 1) Evaluar posibles opciones de uso necesarias a implementar para soportar mejor casos de consumo. 2) Implementar mecanismos automatizados para garantizar la disponibilidad del servicio: ayuda controlar problemas de seguridad e incidentes. 3) Rendimiento de la canalización ante ciertas solicitudes: una oportunidad para mejorar su consumo. Cada aplicación es diferente en su forma de trabajar, entonces el monitoreo puede ayudar a ver oportunidades y comportamiento del consumidor que deben entenderse y gestionarse.

Crea un marco de trabajo que gestione a creación, eliminación y monitoreo de las canalizaciones de datos: usa tecnologias serverless y micro-servicios con autoescalado para ello. Esto permite tener canalizaciones independientes para trabajar dedicadamente al consumidor, entregando escalabilidad en la gestión.

From my experience ... t: Use Delta Lake: utilize Databricks tools such as Delta Lake for reliable data storage and versioning. SQL compatibility: Use SQL as an API for data access to make it easier for different applications to query and interact with the data. Data catalogs: Use Databricks features such as Unity Catalogs to organize and manage metadata, making it easier to find and access datasets. Schema evolution: Overcome challenges related to schema modifications by leveraging Delta Lake's schema evolution capabilities. The most important aspects are the use of standardized data formats such as Delta and SQL as an API for data access to provide a common interface to interact with the data efficiently.