What are the most promising cloud data pipeline trends?

Serverless data pipelines are great way to get a company up and running on cloud from their on-premise complex self-managed datacenter oriented processing. It requires less knowledge of server maintenance and with some simple setup, pipelines can be onboarded to cloud systems. Azure Synapse is a great way to get started on this.

Yes serverless data pipelines are a great trend especially with the rise of AI. It gives developers the advantage of building useful applications without being worried much about server management as this is taken care of on cloud. Azure serverless, Amazon open reach serverless and Google cloud serverless are examples of great platforms that offer a fully managed serverless platform.

All the public cloud providers do give this option, but the best in class is Azure. They have lot of serverless + Low/No-code services which give a lot of ease while development. Though they are optimized, but Serverless sometimes can be costly than their PaaS counterparts. Its strictly advisable to follow best practices while going for serverless architecture in data space.

Advantages of Serverless Data Pipelines:- Simplified Management: Infrastructure concerns alleviated, cloud provider handles scaling and maintenance. Cost Efficiency: Pay-as-you-go model reduces expenses, charging for actual compute resources used. Scalability: Automated scaling manages workload fluctuations without manual intervention. Rapid Development: Developers focus solely on coding and optimizing data processing logic. Flexibility and Agility: Quick updates and changes without disrupting operational flow, ensuring responsiveness to evolving business requirements.

At work, we use these Serverless Data Pipelines to process Terabytes of Data seamlessly performing Transformations and writing and sending to various destinations. One thing we've been working on is the parallelization of these pipelines using Kubernetes deployments.

Incorporating DataOps and CI/CD practices into your data pipeline development can streamline the process, enhance the quality of your data, and provide a more agile and responsive approach to data management.

Another addon we are working on is the development of an infrastructure and data monitoring Bot that alerts the key stakeholders of any noteworthy changes.

DataOps and CI/CD significantly reduced errors, improved collaboration between data engineers and analysts, and accelerated the delivery of reliable data. It enabled us to quickly respond to changing business requirements and ensured that data was consistently accurate and available.

DataOps and CI/CD in data engineering focus on automation, collaboration, and efficiency. DataOps streamlines the data lifecycle through collaboration, automation, version control, testing, monitoring, and deployment. CI/CD, an extension of software practices, automates data pipeline development, testing, and deployment, treating pipelines as code. Both approaches aim to reduce errors, improve efficiency, and accelerate data pipeline delivery.

Change Data Capture (CDC) plays a crucial role in enabling real-time data pipelines to seamlessly transfer data to the cloud. By capturing and replicating data changes as they occur in the source systems, CDC ensures that the most recent and relevant data is continuously updated and transferred to the cloud environment in real time. This facilitates a constant flow of up-to-date information, allowing cloud-based applications and analytics to operate on the latest data, thus enabling organizations to make timely, data-driven decisions and derive actionable insights from the most current data available.

In my experience I have worked in Google Cloud Platform native experience such as Pub/Sub for publishers and subscriptions then Dataflow with Apache Beam for orchestration then Bigquery for Serverless ETL Solution

Real-time data pipelines in data engineering process and transport data with minimal delay. They include data ingestion, stream processing, data transformation, event time handling, storage, and monitoring. Use cases range from IoT and e-commerce to financial services and healthcare. Challenges include managing low latency, handling large data volumes, and stream processing complexity. These pipelines enable quick, data-driven decision-making.

Real time data processing has been slowly becoming the norm. Most of the data processing engines offer the combined APIs for both batch and stream processing and that has made the adaption quicker

Stream Processing: Use Apache Kafka for data ingestion and stream processing to handle high-throughput data. Micro-batching: Employ Apache Spark for processing data in near-real-time using micro-batches. Event-Driven Architecture: Implement complex event processing with Apache Flink for low-latency streaming. Managed Services: Utilize Google Cloud Dataflow for fully managed stream and batch data processing. Scaling: Ensure the pipeline can scale horizontally to manage load dynamically. State Management: Maintain state for accurate real-time analytics. Fault Tolerance: Design for high availability with checkpointing and failover strategies. Monitoring: Implement comprehensive monitoring and alerting for system health and performance.

n my work with data mesh on customer projects, I've seen firsthand how it fosters a sense of ownership among teams, treating data as a valuable product. This approach decentralizes data governance, allowing domain-specific teams to manage their data assets directly, enhancing quality and accessibility. It's more of an organizational shift than a technical one, requiring a new governance model that supports autonomy and inter-domain interoperability. By implementing data mesh through platforms like GCP / BigQuery, we've enabled more agile and collaborative data management across various stakeholders.

Data Mesh is an approach to data management that focuses on decentralization. Data is organized around domains, treated as products, and managed by domain teams. Self-service data infrastructure, metadata, and standardization are key components. This approach aims to improve scalability and data utilization in large organizations.

Not every company can afford a big data engg team, especially in the early stages. Early MVP solutions benefit from low/no-code, to have teams be agile and focused on the highest value work.

Working in a large multinational processing Terabytes of Data in an Environment complying with Data Maturity Model Level 5, I can say that Low-Code/No-Code has come a long way and is now able to cater to Enterprise requirements. BTW, I have been using various LCNC platforms for quite some years in various projects but not at this scale!

Low code and no code data pipelines simplify the creation and management of data pipelines without extensive coding. Low code platforms are for users with some technical expertise and offer visual development, pre-built components, automation, and customization. No code platforms target business users without coding skills and provide point-and-click interfaces with templates and wizards. Both are used for data integration, ETL, data migration, and data synchronization, empowering non-technical users to work with data efficiently. However, they may have limitations for highly complex scenarios, and data engineers may still be needed for advanced use cases.

While I love coding, I think most of the standard, boilerplate code of setting up connections between resources and moving data could be abstracted away into LCNC blocks. This is especially relevant for complex pipelines with multiple levels of nesting/hierarchy. LCNC allows to quickly overview what is happening in the pipeline and its sub-pipelines and what could be fixed. This is invaluable for long-term maintenance. Thus, my ideal tools are the ones that allow working at different levels of abstraction: LCNC for standard data operations and PySpark notebooks (for example) for more customized, complex coding needs. For example, in Microsoft stack, this paradigm is manifested in Azure Data Factory combined with Databricks notebooks.

In my opinion , there are a few more things to consider - batch or stream processing - who are the end users ; pre-processing of data for ML workflows or data warehouse - space for improvements to consider in data pipelines such as capabilities for running spark operator on eks using emr runtime , Apache Livy , serverless spark , kubeflow integration

My humble 2 cents would be around the fact that many companies jump on the buzz words and try to do streaming with 10 GBS of data before they understand what's the end goal, who is the end user and what is the fastest/easiest way to come to a result is.