Data Pipeline

Jupyterlab - Python

By using ZebClient Analytics (lakeuno), as a data engineering platform for AI and ML workloads, users gain access to a wide range of data pipeline features from Kubeflow, Airflow, and Airbyte. Coupled with Jupyter Hub and Elyra pipeline editors, this integration allows users to efficiently design, develop, and deploy complex data processing pipelines while maintaining a consistent development environment. The solution also offers advanced visualization tools for pipeline design. Here are some of the capabilities that ZebClient Analytics's users benefit from through this integration:

  1. Visual pipeline design: Elyra pipeline editors provide an intuitive graphical interface for designing complex data processing pipelines, allowing users to visualize their workflows and easily manage dependencies between different stages. This can lead to more efficient and error-free pipeline development as compared to traditional scripting methods.

  2. Integrated data preprocessing: ZebClient Analytics's integration of Elyra pipeline editors enables users to perform data preprocessing tasks within the same environment where they design their pipelines, reducing context switching and improving overall productivity. This can include transformations such as data cleaning, feature engineering, and data aggregation.

  3. Seamless workflow: The integration of Kubeflow, Airflow, Airbyte, and Elyra pipeline editors within Jupyter Hub creates a seamless workflow for users as they progress through the various stages of their AI and ML projects. Users can easily switch between pipeline design, data processing, model development, and testing without leaving their Jupyter environment, which saves time and reduces context switching.

  4. Scalability: ZebClient Analytics's integration of these pipeline tools within Jupyter Hub, including Elyra pipeline editors, allows users to handle large-scale AI and ML projects by offering distributed computing capabilities for data processing tasks. This ensures that even the most demanding data processing pipelines can be executed efficiently and effectively within the ZebClient Analytics environment.

  5. Enhanced collaboration: The integration of these pipeline tools within Jupyter Hub, including Elyra pipeline editors, enables users to collaborate more effectively on AI and ML projects by allowing them to share pipelines, notebooks, and models with their team members. Real-time feedback and communication help streamline the development process and improve overall productivity.

  6. Flexibility: ZebClient Analytics offers a flexible platform for data processing pipelines as it supports multiple tools for different use cases within its integrated environment, including Elyra pipeline editors. Users can choose the most appropriate tool for their specific project requirements, ensuring that they have the right tool for the job while maintaining consistency across their workflows.

  7. Extensibility: ZebClient Analytics allows users to extend its functionality by integrating custom tools or components into their data processing pipelines using Elyra pipeline editors. This flexibility caters to the unique needs of different organizations and projects while ensuring a consistent development experience.

  8. Simplified deployment and management: By offering a single platform for managing data processing pipelines, machine learning models, and Jupyter Notebooks/Labs, ZebClient Analytics simplifies the deployment and management process for AI and ML projects through its integration with Elyra pipeline editors. This centralized approach reduces administrative overhead and improves overall operational efficiency.


JupySQL allows you to run SQL and plot large datasets in Jupyterlab via a %sql, %%sql, and %sqlplot magics. JupySQL is compatible with all major databases (e.g., PostgreSQL, MySQL, SQL Server), data warehouses (e.g., Snowflake, BigQuery, Redshift), and embedded engines (SQLite, and DuckDB).


An open-source machine learning platform built to deploy, manage, and scale machine learning models using containers in Kubernetes clusters. It's designed to simplify the process of building, deploying, and managing ML workflows at scale while ensuring reproducibility. Key features of Kubeflow include:

  1. Machine Learning Pipelines: Kubeflow provides an end-to-end solution for building, deploying, and managing machine learning pipelines using customizable components like Argo ML (a custom ML training runner), TensorFlow Serving, and Jupyter Notebooks. This enables users to create complex workflows that include data preparation, model training, evaluation, and serving.

  2. Scalability: Kubeflow leverages the power of Kubernetes to scale ML workflows and models as needed by adding or removing resources based on demand. It also supports distributed training using TensorFlow and Horovod, making it ideal for large-scale ML projects.

  3. Reproducibility: Kubeflow ensures reproducible results by keeping track of the entire ML pipeline from data preparation to model serving, enabling users to recreate their ML workflows exactly as they were originally defined.

  4. Integration: Kubeflow offers seamless integration with popular machine learning frameworks (TensorFlow, PyTorch, Scikit-learn), container registries (Docker Hub, Google Container Registry), and cloud providers like GCP and AWS. It also supports integration with custom components using containers.

  5. User Interface: Kubeflow provides a web UI for managing ML pipelines, monitoring experiments, and visualizing model performance, making it easy to manage complex ML workflows at scale.

  6. Extensibility: Kubeflow has an active community contributing new features, integrations, and tools to expand its capabilities, ensuring it stays up-to-date with the latest advancements in machine learning technology.

  7. Security: Kubeflow supports various security features like RBAC (Role-Based Access Control), SSL encryption, and integration with external identity providers for secure access to ML workflows and data. Kubeflow is suitable for organizations looking to build, deploy, and manage machine learning models at scale in a containerized environment while ensuring reproducibility and security. Its powerful features make it an ideal choice for data engineering and data science teams working on large-scale ML projects.


An open-source platform designed for programming and orchestrating complex data pipelines and workflows. It provides an easy-to-use interface for creating, scheduling, and monitoring tasks and their dependencies in a workflow. Airflow is written primarily in Python and can be deployed on a single machine or a cluster of machines for improved performance.Key features of Airflow include:

  1. Extensible Programming Interface: Airflow defines tasks as code (Python functions) in an Directed Acyclic Graph (DAG). It then schedules and executes these tasks based on predefined schedules or triggers, allowing users to build custom workflows tailored to their needs.

  2. Scalability: Airflow can be scaled horizontally by adding more workers (Masters) to handle more tasks concurrently. It also supports distributed processing using Celery and RabbitMQ for improved performance when dealing with large datasets.

  3. Extensibility: Airflow has a rich ecosystem of community-built operators, sensors, hooks, and integrations, enabling users to extend its capabilities to various data sources (Hadoop, S3, GCS), databases(PostgreSQL, MySQL), programming languages, and more.

  4. User Interface: Airflow provides an intuitive web interface for monitoring the status of tasks, observing task logs, and visualizing the entire DAG structure, making it easy to manage complex workflows at a glance.

  5. Extensible Alerts: Airflow offers customizable alerts based on events like task failure, task retries, or schedule misses, ensuring users are notified promptly when issues arise.

  6. Scalable and Flexible: Airflow can be deployed on a single machine, cluster of machines, or even in the cloud using managed Kubernetes services for improved performance and flexibility.

  7. Versioning: Airflow keeps track of past DAG runs and allows users to easily compare changes made to their workflows over time, ensuring they have an audit trail of all modifications and can roll back to previous versions if needed.

  8. Security: Airflow supports various security features like authentication using LDAP, OAuth2, or other external identity providers, SSL encryption, and fine-grained access control for users and projects.

Airflow is widely used in data engineering and data science teams that work with complex data pipelines and workflows involving multiple tasks and dependencies. It simplifies the process of building, scheduling, and monitoring workflows, while its extensibility and customizability make it a versatile tool for various use cases.


An open-source data integrator that allows users to replicate data from various sources into different destinations in real-time or near real-time. The integration of Airbyte within ZebClient Analytics, a data engineering platform for AI and ML workloads, offers several advantages to users:

  1. Real-time data ingestion: Airbyte enables users to stream real-time data from various sources such as databases, APIs, or messaging systems into their data processing pipelines in ZebClient Analytics. This real-time data access can be crucial for applications that require up-to-the-minute insights, enabling faster decision-making and improved operational efficiency.

  2. Flexible connectors: Airbyte offers a wide range of connectors for popular data sources and destinations, allowing users to easily replicate data from various systems without having to write custom code or scripts. This saves development time and reduces the need for multiple tools to handle different data integrations.

  3. Customizable transformations: Airbyte allows users to apply custom transformations on the data being replicated, providing more control over the data ingestion process within ZebClient Analytics. Users can clean, filter, aggregate, or enrich their data as it is being replicated, ensuring that they receive high-quality data for their AI and ML workloads.

  4. Scalable architecture: Airbyte's event-driven, scalable architecture allows users to handle large volumes of data efficiently within ZebClient Analytics. This is essential when dealing with big data processing pipelines or multiple data integrations in parallel.

  5. Integration with other tools: The integration of Airbyte within ZebClient Analytics allows users to leverage its capabilities alongside other popular data engineering tools like Kubeflow, Airflow, and Jupyter Hub. Users can easily design complex data processing pipelines that incorporate real-time data ingestion using Airbyte.

  6. Open-source: Airbyte being an open-source project allows users to customize its functionality to meet their specific needs without any licensing costs. This flexibility makes it an attractive choice for organizations looking for cost-effective solutions while maintaining control over their data processing pipelines within ZebClient Analytics.

  7. Continuous data replication: Airbyte supports continuous data replication, ensuring that data is always up-to-date in the target systems. This can be crucial for applications requiring real-time insights or for maintaining data consistency across different systems in ZebClient Analytics.

  8. Improved data access and integration: The addition of Airbyte within ZebClient Analytics enhances the overall capability of the platform by providing seamless data access and integration from various sources, enabling users to build more complex AI and ML applications efficiently.

Video Demonstrating the Build of a Data Pipeline

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