Automating Intelligent ETL with Data Live Tables

Given the fast-paced changes in the market environment and the need to retain competitive advantage, businesses must address the challenges, improve efficiencies, and deliver high-quality data reliably and on time. This is possible only by automating ETL processes.

The Databricks Lakehouse Platform offers Delta Live Tables (DLT), a new cloud-native managed service that facilitates the development, testing, and operationalization of data pipelines at scale, using a reliable ETL framework. DLT simplifies the development and management of ETL with:

• Declarative pipeline development
• Automatic data testing
• Monitoring and recovery with deep visibility

With Delta Live Tables, end-to-end data pipelines can be defined easily by specifying the source of the data, the logic used for transformation, and the target state of the data. It can eliminate the manual integration of siloed data processing tasks. Data engineers can also ensure data dependencies are maintained across the pipeline automatically and apply data management for reusing ETL pipelines. Incremental or complete computation for each table during batch or streaming run can be specified based on need.

Benefits of DLT

The DLT framework can help build data processing pipelines that are reliable, testable, and maintainable. Once the data engineers provide the transformation logic, DLT can orchestrate the task, manage clusters, monitor the process and data quality, and handle errors. The benefits of DLT include;

Assured Data Quality

Delta Live Tables can prevent bad data from reaching the tables by validating and checking the integrity of the data. Using predefined policies on errors such as fail, alert, drop, or quarantining data, Delta Live Tables can ensure the quality of the data to improve the outcomes of BI, machine learning, and data science. It can also provide visibility into data quality trends to understand how the data is evolving and what changes are necessary.

Improved Pipeline Visibility

DLT can monitor pipeline operations by providing tools that enable visual tracking of operational stats and data lineage. Automatic error handling and easy replay can reduce downtime and accelerate maintenance with deployment and upgrades at the click of a button.

Improve Regulatory Compliance

The event log can automatically capture information related to the table for analysis and auditing. DLT can provide visibility into the flow of data in the organization and improve regulatory compliance.

Simplify Deployment and Testing of Data Pipeline

DLT can enable data to be updated and lineage information to be captured for different copies of data using a single code base. It can also enable the same set of query definitions to be run through the development, staging, and production stages.

Simplify Operations with Unified Batch and Streaming

Build and run of batch and streaming pipelines can be centralized, and the operational complexity can be effectively minimized with controllable and automated refresh settings.

Concepts Associated with Delta Live Tables

The concepts used in DLT include:

Pipeline: A Directed Acyclic Graph that can link data sources with destination datasets

Pipeline Setting: Pipeline settings can define configurations such as;

• Notebook
• Target DB
• Running mode
• Cluster config
• Configurations (Key-Value Pairs).

Dataset: The two types of datasets DLT supports include Views and Table, which, in turn, are of two types: Live and Streaming.

Pipeline Modes: Delta Live provides two modes for development:

Development Mode: The cluster is reused to prevent restarts and disable pipeline retries for detecting and fixing errors.

Production Mode: Cluster restart for recoverable errors such as stale credentials or memory leak and execution is retried for specific errors.

Editions: DLT comes in various editions to suit the different needs of the customers such as:

• Core for streaming ingest workload
• Pro for core features + CDC, streaming ingest, and table updation based on changes to the source data
• Advanced where in addition to core and pro features, data quality constraints are also available

Delta Live Event Monitoring: Delta Live Table Pipeline event log is stored under the storage location in /system/events.

Indium for Building Reliable Data Pipelines Using DLT

Indium is a recognized data engineering company with an established practice in Databricks. We offer ibriX, an Indium Databricks AI Platform, that helps businesses become agile, improve performance, and obtain business insights efficiently and effectively.

Our team of Databricks experts works closely with customers across domains to understand their business objectives and deploy the best practices to accelerate growth and achieve the goals. With DLT, Indium can help businesses leverage data at scale to gain deeper and meaningful insights to improve decision-making.

FAQs

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With the advent of big data technologies, many challenges in dealing with large datasets have been addressed. But in a typical data processing scenario, when a data set is too large to be processed by a single machine or when a single machine may not contain the data to respond to user queries, it requires the processing power of multiple machines. These scenarios are becoming increasingly complex as many applications, devices, and social platforms need data in an organization, and this is where distributed data processing methods are best implemented.  

Understanding Distributed Data Processing 

Distributed data processing consists of a large volume of data that flows through variable sources into the system. There are various layers in that system that manage this data ingestion process.  

At first, the data collection and preparation layer collects the data from different sources, which is further processed by the system. However, we know that any data gathered from external sources are mainly raw data such as text, images, audio, and forms. Therefore, the preparation layer is responsible for converting the data into a usable and standard format for analytical purposes. 

Meanwhile, the data storage layer primarily handles data streaming in real-time for performing analytics with the help of in-memory distributed caches for storing and managing data. Similarly, if the data is required to be processed in the conventional approach, then batch processing is performed across distributed databases, effectively handling big data.  

Next is the data processing layer, which can be considered the logical layer that processes the data. This layer allows various machine learning solutions and models for performing predictive, descriptive analytics to derive meaningful business insights. Finally, there is the data visualization layer consisting of dashboards that allows visualization of the data and reports after performing different analytics using graphs and charts for better interpretation of the results. 

In the quest to find new approaches to distribute processing power, application programs, and data, distributed data engineering solutions  is adopted to enable the distribution of applications and data among various interconnected sites to complement the increasing need for information in the organizations. However, an organization may opt for a centralized or a decentralized data processing system, depending on their requirements.  

Benefits of Distributed Data Processing 

The critical benefit of processing data within a distributed environment is the ease at which tasks can be completed with significantly lesser time as data is accessible from multiple machines that execute the tasks parallelly instead of a single machine running requests in a queue. 

As the data is processed faster, it is a cost-effective approach for businesses, and running workloads in a distributed environment meets crucial aspects of scalability and availability in today’s fast-paced environment. In addition, since data is replicated across the clusters, there is less likelihood of data loss.

Challeges of Distributed Data Processing 

The entire process of setting up and working with a distributed system is complex.  

With large enterprises compromised data security, coordination problems, occasional performance bottlenecks due to non-performing terminals in the system and even high costs of maintenances are seen as major issues. 

How is Databricks Platform Used for Distributed Data Processing? 

The cloud data platforms Databricks Lakehouse  helps to perform analytical queries, and there is  a provision of Databricks SQL for working with business intelligence and analytical tasks atop the data lakes. Analysts can query data sets using standard SQL and have great features for integrating business intelligence tools like Tableau. At the same time, the Databricks platform allows working with different workloads encompassing machine learning, data storage, data processing, and streaming analytics in real time. 

The immediate benefit of a Databricks architecture is enabling seamless connections to applications and effective cluster management. Additionally, using databricks provides a simplified setup and maintenance of the clusters, which makes it easy for developers to create the ETL pipelines. These ETL pipelines ensure data availability in real-time across the organization leading to better collaborative efforts among cross-functional teams.  

With the Databricks Lakehouse platform, it is now easy to ingest and transform batch and streaming data leading to reliable production workflows. Moreover, Databricks ensure clusters scale and terminate automatically as per the usage. Since the data ingestion process is simplified, all analytical solutions, AI, and other streaming applications can be operated from a single place.  

Likewise, automated ETL processing is provided to ensure raw data is immediately transformed to be readily available for analytics and AI applications. Not only the data transformation but automating ETL processing allows for efficient task orchestration, error handling, recovery, and performance optimization. Orchestration enables developers to work with diverse workloads, and the data bricks workflow can be accessed with a host of features using the dashboard, improving tracking and monitoring of performance and jobs in the pipeline. This approach continuously monitors performance, data quality, and reliability metrics from various perspectives.  

In addition, Databricks offers  a data processing engine compatible with Apache Spark APIs that speeds up the work by automatically scaling multiple nodes. Another critical aspect of this Databricks platform is enabling governance of all the data and AI-based applications with a single model for discovering, accessing, and securing data sharing across cloud platforms. 

Similarly, there is support for Datbricks SQL within the Databricks Lakehouse, a serveless data warehouse capable of running any SQL and business intelligence applications at scale. 

Databricks Services From Indium: 

With deep expertise in Databricks Lakehouse, Advanced Analytics & Data Products, Indium Software provides wide range of services to help our clients’ business needs. Indium’s propreitory solution accelerator iBriX is a packaged combination of AI/ML use cases, custom scripts, reusable libraries, processes, policies, optimization techniques, performance management with various levels of automation including standard operational procedures and best practices. 

To know more about iBriX and the services we offer, write to info@www.indiumsoftware.com.  

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Over the years, the need to simplify Databricks deployment on AWS had become a persistent demand due to the complexity involved. When deploying Databricks on AWS, customers had to constantly between consoles as given in a very detailed documentation. To deploy the workspace, customers had to:

• Configure a virtual private cloud (VPC)
• Set up security groups
• Create a cross-account AWS Identity and Access Management (IAM) role
• Add all AWS services used in the workspace

This could take more than an hour and needed a Databricks solutions architect familiar with AWS to guide the process.

To make matters simple and easy and enable self-service, the company offers Quick Start in collaboration with Amazon Web Services (AWS). This is an automated reference deployment tool integrating AWS best practices to leverage AWS Cloud Formation templates and deploy key technologies on AWS.

Incorporating AWS Best Practices

Best Practice #1 – Ready, Steady, Go

Make it easy even for non-technical customers to get Databricks up and running in minutes. Quick Starts allows customers to sign in to the AWS Management Console and deploy Databricks within minutes after selecting the CloudFormation template and Region by filling in the parameter values required for the purpose and deploy. Quick Starts is applicable to several environments and the architecture is designed such that customers using any environment can leverage it.

Best Practice #2 – Automating Installation

Deployment of Databricks involved installing and configuring several components manually earlier. This is a very slow process, prone to errors and reworks. The customers had to refer to a document to get it right and this was proving to be difficult. By automating the process, AWS cloud deployments can be speeded up effectively and efficiently.

Best Practice #3 – Security from the Word Go

One of the AWS best practices is the focus on security and availability. When deploying Databricks, this focus should be integrated right from the beginning. For effective security and availability, aligning it with the AWS user management to allow one-time IAM will provide access to the environment with appropriate controls. This should be supplemented with AWS Security Token Service (AWS STS) to authenticate user requests for temporary, limited-privilege credentials.

Best Practice #4 High Availability

As the environment spans two Availability Zones, it ensures a highly available architecture. Add a Databricks- or customer-managed virtual private cloud (VPC) to the customer’s AWS account and configure it with private subnets and a public subnet. This will provide customers with access to their own virtual network on AWS. In the private subnets, Databricks clusters of Amazon Elastic Compute Cloud (Amazon EC2) instances can be added along with additional security groups to ensure secure cluster connectivity. In the public subnet, outbound internet access can be provided with a network address translation (NAT) gateway. Use Amazon Simple Storage Service (Amazon S3) bucket for storing objects such as notebook revisions, cluster logs, and job results.

The benefits of using these best practices is that creating and configuring the AWS resources required to deploy and configure the Databricks workspace can be automated easily. It doesn’t need solutions architects to undergo extensive training to the configurations and can be an intuitive process. This will help them remain updated with the latest product enhancements, security upgrades, and user experience improvements without difficulty.

Since the launch of Quick Starts in September 2020, Databricks deployment on AWS has become much simpler, resulting in:

• Deployment time takes only 5 minutes as against the earlier 1 hour
• 95% lower deployment errors

As it incorporates the best practices of AWS and is co-developed by AWS and Databricks, the solution answers the need of its customers to quickly and effectively deploy Databricks on AWS.

Indium – Combining Technology with Experience

Indium Software is an AWS and Databricks solution provider with a battalion of data experts who can help you with deploying Databricks on AWS to set you off on your cloud journey. We work with our customers closely to understand their business goals and smooth digital transformation by designing solutions that cater to their goals and objectives.

While Quick Starts is a handy tool that accelerates the deployment of Databricks on AWS, we help design the data lake architecture to optimize cost and resources and maximize benefits. Our expertise in DevSecOps ensures a secure and scalable solution that is highly available with permission-based access to enable self-service with compliance.

Some of the key benefits of working with Indium on Databricks deployments include:

• More than 120 person-years of Spark expertise
• Dedicated Lab and COE for Databricks
• ibriX – Homegrown Databricks Accelerator for faster Time-to-market
• Cost Optimization Framework – Greenfield and Brownfield engagements
• E2E Data Expertise – Lakehouse, Data Products, Advanced Analytics, and ML Ops
• Wide Industry Experience – Healthcare, Financial Services, Manlog, Retail and Realty

FAQs

How to create a Databrick in AWS?

In the free trial, you can sign up by clicking the Try Databricks button at the top of the page or on AWS Marketplace.

How can one store and access data on Databricks and AWS?

All data can be stored and managed on a simple, open lakehouse platform. Databricks on AWS allows the unification of all analytics and AI workloads by combining the best of data warehouses and data lakes.

How can Databricks connect to AWS?

AWS Glue allows Databricks to be integrated and Databricks table metadata to be shared from a centralized catalog across various Databricks workspaces, AWS services, AWS accounts, and applications for easy access.

The post Deploying Databricks on AWS: Key Best Practices to Follow appeared first on Indium.