Databricks with Azure Past and Present

 

Let's dive into the evolution of Azure Databricks and its performance differences.

Azure Databricks is a powerful analytics platform built on Apache Spark, designed to process large-scale data workloads. It provides a collaborative environment for data engineers, data scientists, and analysts. Over time, Databricks has undergone significant changes, impacting its performance and capabilities.

Previous State:

In the past, Databricks primarily relied on an open-source version of Apache Spark. While this version was versatile, it had limitations in terms of performance and scalability. Users could run Spark workloads, but there was room for improvement.

Current State:

Today, Azure Databricks has evolved significantly. Here’s what’s changed:

1. Optimized Spark Engine:

   • Databricks now offers an optimized version of Apache Spark. This enhanced engine provides 50 times increased performance compared to the open-source version.
   • Users can leverage GPU-enabled clusters, enabling faster data processing and higher data concurrency.
   • The optimized Spark engine ensures efficient execution of complex analytical tasks.

2. Serverless Compute:

   • Databricks embraces serverless architectures. With serverless compute, the compute layer runs directly within your Azure Databricks account.
   • This approach eliminates the need to manage infrastructure, allowing users to focus solely on their data and analytics workloads.
   • Serverless compute optimizes resource allocation, scaling up or down as needed.

Performance Differences:

Let’s break down the performance differences:

1. Speed and Efficiency:

   • The optimized Spark engine significantly accelerates data processing. Complex transformations, aggregations, and machine learning tasks execute faster.
   • GPU-enabled clusters handle parallel workloads efficiently, reducing processing time.

2. Resource Utilization:

   • Serverless compute ensures optimal resource allocation. Users pay only for the resources consumed during actual computation.
   • Traditional setups often involve overprovisioning or underutilization, impacting cost-effectiveness.

3. Concurrency and Scalability:

   • Databricks’ enhanced Spark engine supports high data concurrency. Multiple users can run queries simultaneously without performance degradation.
   • Horizontal scaling (adding more nodes) ensures seamless scalability as workloads grow.

4. Cost-Effectiveness:

• Serverless architectures minimize idle resource costs. Users pay only for active compute time.
• Efficient resource utilization translates to cost savings.

Currently, Azure does not use BLOB storage for Databrick compute plane, instead ADSL Gen 2, also known as Azure Data Lake Storage Gen2, is a powerful solution for big data analytics built on Azure Blob Storage. Let’s dive into the details:

1. What is a Data Lake?

   • A data lake is a centralized repository where you can store all types of data, whether structured or unstructured.
   • Unlike traditional databases, a data lake allows you to store data in its raw or native format, without conforming to a predefined structure.
   • Azure Data Lake Storage is a cloud-based enterprise data lake solution engineered to handle massive amounts of data in any format, facilitating big data analytical workloads.

2. Azure Data Lake Storage Gen2:

   • Convergence: Gen2 combines the capabilities of Azure Data Lake Storage Gen1 with Azure Blob Storage.
   • File System Semantics: It provides file system semantics, allowing you to organize data into directories and files.
   • Security: Gen2 offers file-level security, ensuring data protection.
   • Scalability: Designed to manage multiple petabytes of information while sustaining high throughput.
   • Hadoop Compatibility: Gen2 works seamlessly with Hadoop and frameworks using the Apache Hadoop Distributed File System (HDFS).
   • Cost-Effective: It leverages Blob storage, providing low-cost, tiered storage with high availability and disaster recovery capabilities.

3. Implementation:

   • Unlike Gen1, Gen2 isn’t a dedicated service or account type. Instead, it’s implemented as a set of capabilities within your Azure Storage account.
   • To unlock these capabilities, enable the hierarchical namespace setting.
   • Key features include:
     • Hadoop-compatible access: Designed for Hadoop and frameworks using the Azure Blob File System (ABFS) driver.
     • Hierarchical directory structure: Organize data efficiently.
     • Optimized cost and performance: Balances cost-effectiveness and performance.
     • Finer-grained security model: Enhances data protection.
     • Massive scalability: Handles large-scale data workloads.

Conclusion:

Azure Databricks has transformed from its initial open-source Spark version to a high-performance, serverless analytics platform. Users now benefit from faster processing, efficient resource management, and improved scalability. Whether you’re analyzing data, building machine learning models, or running complex queries, Databricks’ evolution ensures optimal performance for your workloads. 

Redhat Openshift for Data Science Project

 

Photo by Tim Mossholder

Red Hat OpenShift Data Science is a powerful platform designed for data scientists and developers working on artificial intelligence (AI) applications. Let’s dive into the details:

1. What is Red Hat OpenShift Data Science?

   • Red Hat OpenShift Data Science provides a fully supported environment for developing, training, testing, and deploying machine learning models.
   • It allows you to work with AI applications both on-premises and in the public cloud.
   • You can use it as a managed cloud service add-on to Red Hat’s OpenShift cloud services or as self-managed software that you can install on-premise or in the public cloud.

2. Key Features and Benefits:

   • Rapid Development: OpenShift Data Science streamlines the development process, allowing you to focus on building and refining your models.
   • Model Training: Train your machine learning models efficiently within the platform.
   • Testing and Validation: Easily validate your models before deployment.
   • Deployment Flexibility: Choose between on-premises or cloud deployment options.
   • Collaboration: Work collaboratively with other data scientists and developers.

3. Creating a Data Science Project:

   • From the Red Hat OpenShift Data Science dashboard, you can create and configure your data science project.
   • Follow these steps:
     • Navigate to the dashboard and select the Data Science Projects menu item.
     • If you have existing projects, they will be displayed.
     • To create a new project, click the Create data science project button.
     • In the pop-up window, enter a name for your project. The resource name will be automatically generated based on the project name.
     • You can then configure various options for your project.

4. Data Science Pipelines:

   • Enhance your data science projects by building portable machine learning workflows using data science pipelines.
   • These pipelines use Docker containers to standardize and automate machine learning workflows.
   • With pipelines, you can develop and deploy your data science models more efficiently¹².

In summary, Red Hat OpenShift Data Science provides a robust platform for data scientists to create, train, and deploy machine learning models, whether you’re working on-premises or in the cloud. It’s a valuable tool for data science projects, offering flexibility, collaboration, and streamlined development processes.

Let’s explore how you can leverage Red Hat OpenShift Data Science in conjunction with a Kubernetes cluster for your data science project. I’ll provide a step-by-step guide along with an example.

Using OpenShift Data Science with Kubernetes for Data Science Projects

1. Set Up Your Kubernetes Cluster:

   • First, ensure you have a functional Kubernetes cluster. You can use a managed Kubernetes service (such as Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE), or Amazon Elastic Kubernetes Service (EKS)) or set up your own cluster using tools like kubeadm or Minikube.
   • Make sure your cluster is properly configured and accessible.

2. Install Red Hat OpenShift Data Science:

   • Deploy OpenShift Data Science on your Kubernetes cluster. You can do this by installing the necessary components, such as the OpenShift Operator, which manages the data science resources.
   • Follow the official documentation for installation instructions specific to your environment.

3. Create a Data Science Project:

   • Once OpenShift Data Science is up and running, create a new data science project within it.
   • Use the OpenShift dashboard or command-line tools to create the project. For example:

     oc new-project my-data-science-project
     

4. Develop Your Data Science Code:

   • Write your data science code (Python, R, etc.) and organize it into a Git repository.
   • Include any necessary dependencies and libraries.

5. Create a Data Science Pipeline:

   • Data science pipelines in OpenShift allow you to define a sequence of steps for your project.
   • Create a Kubernetes Custom Resource (CR) that describes your pipeline. This CR specifies the steps, input data, and output locations.
   • Example pipeline CR:

     apiVersion: datascience.openshift.io/v1alpha1
     kind: DataSciencePipeline
     metadata:
       name: my-data-pipeline
     spec:
       steps:
         - name: preprocess-data
           image: my-preprocessing-image
           inputs:
             - dataset: my-dataset.csv
           outputs:
             - artifact: preprocessed-data.csv
         # Add more steps as needed
     

6. Build and Deploy Your Pipeline:

   • Build a Docker image for each step in your pipeline. These images will be used during execution.
   • Deploy your pipeline using the OpenShift Operator. It will create the necessary Kubernetes resources (Pods, Services, etc.).
   • Example:

     oc apply -f my-data-pipeline.yaml
     

7. Monitor and Debug:

   • Monitor the progress of your pipeline using OpenShift’s monitoring tools.
   • Debug any issues that arise during execution.

8. Deploy Your Model:

   • Once your pipeline completes successfully, deploy your trained machine learning model as a Kubernetes Deployment.
   • Expose the model using a Kubernetes Service (LoadBalancer, NodePort, or Ingress).

9. Access Your Model:

   • Your model is now accessible via the exposed service endpoint.
   • You can integrate it into your applications or use it for predictions.

Example Scenario: Sentiment Analysis Model

Let’s say you’re building a sentiment analysis model. Here’s how you might structure your project:

1. Data Collection and Preprocessing:

   • Collect tweets or reviews (your dataset).
   • Preprocess the text data (remove stopwords, tokenize, etc.).

2. Model Training:

   • Train a sentiment analysis model (e.g., using scikit-learn or TensorFlow).
   • Save the trained model as an artifact.

3. Pipeline Definition:

   • Define a pipeline that includes steps for data preprocessing and model training.
   • Specify input and output artifacts.

4. Pipeline Execution:

   • Deploy the pipeline.
   • Execute it to preprocess data and train the model.

5. Model Deployment:

   • Deploy the trained model as a Kubernetes service.
   • Expose the service for predictions.

Remember that this is a simplified example. In practice, your data science project may involve more complex steps and additional components. OpenShift Data Science provides the infrastructure to manage these processes efficiently within your Kubernetes cluster.

https://developers.redhat.com/articles/2023/01/11/developers-guide-using-openshift-kubernetes