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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Some of the key technologies that enable the data lakehouse to provide these benefits include:

• Layers of metadata
• Improved SQL execution enabled by new query engine designs
• optimized access for data science and machine learning tools.

Data Lakes for Improved Performance

Metadata layers track the files that can be a part of different table versions to enable ACID-compliant transactions. They support streaming I/O without the need for message buses such as Kafka), facilitating accessing older versions of the table, enforcement and evolution of schema, and validating data.

But among these features, what makes the data lake popular is its performance with the introduction of new query engine designs for SQL analysis. In addition, some optimizations include:

• Hot data caching in RAM/SSDs
• Cluster co-accessed data layout optimization
• Statistics, indexes, and other such auxiliary data structures
• Vectorized execution on modern CPUs

This makes data lakehouse performance on large datasets comparable to other popular TPC-DS benchmark-based data warehouses. Being built on open data formats such as Parquet makes access to data easy for data scientists and machine learning engineers in the lakehouse.

Indium’s capabilities with Databricks services: UX Enhancement & Cross Platform Optimization of Healthcare Application

Easy Steps to Building Databricks Data Lakehouse on AWS

As businesses increase their adoption of AI and analytics and scale up, businesses can leverage Databricks consulting services to experience the benefits of their data by keeping it simple and accessible. Databricks provides a cost-effective solution through its pay-as-you-go solution on Databricks AWS to allow the use of existing AWS accounts and infrastructure.

Databricks on AWS is a collaborative workspace for machine learning, data science, and analytics, using the Lakehouse architecture to process large volumes of data and accelerate innovation. The Databricks Lakehouse Platform, forming the core of the AWS ecosystem, integrates easily and seamlessly with popular Data and AI services such as S3 buckets, Kinesis streams, Athena, Redshift, Glue, and QuickSight, among others.

Building a Databricks Lakehouse on AWS is very easy and involves:

Quick Setup: For customers with AWS partner privileges, setting up Databricks is as simple as subscribing to the service directly from their AWS account without creating a new account. The Databricks Marketplace listing is available in the AWS Marketplace and can be accessed through a simple search. A self-service Quickstart video is available to help businesses create their first workspace.

Smooth Onboarding: The Databricks pay-as-you-go service can be set up using AWS credentials. Databricks allows the account settings and roles in AWS to be preserved, accelerating the setting up and the kick-off of the Lakehouse building.

Pay Per Use: The Databricks on AWS is a cost-effective solution as the customers have to pay based on the use of resources. The billing is linked to their existing Enterprise Discount Program, enabling them to build a flexible and scalable lakehouse on AWS based on their needs.

Try Before Signing Up: AWS customers can opt for a free 14-day trial of Databricks before signing up for the subscription. The billing and payment can be consolidated under their already-present AWS management account.

Benefits of Databricks Lakehouse on AWS

Apart from a cost-effective, flexible and scalable solution for improved management of AI and analytics workloads, some of the other benefits include:

• Supporting AWS Graviton2-based Amazon Elastic Compute Cloud (Amazon EC2) instances for 3x improvement in performance
• Exceptional price-performance ensured by Graviton processors for workloads running in EC2
• Improved performance by using Photon, the new query engine from Databricks Our Engineering team ran benchmark tests and discovered that Graviton2-based

It might be interesting to read on End-To-End ML Pipeline using Pyspark and Databricks (Part I)

Indium–A Databricks Expert for Your AI/Analytics Needs

Indium Software is a leading provider of data engineering, machine learning, and data analytics solutions. An AWS partner, we have an experienced team of Databricks experts who can build Databricks Lakehouse on AWS quickly to help you manage your AI and analytics workloads better.

Our range of services includes: Data Engineering Solutions: Our quality engineering practices optimize data fluidity from origin to destination.

BI & Data Modernization Solutions: Improve decision making through deeper insights and customized, dynamic visualization

Data Analytics Solutions: Leverage powerful algorithms and techniques to augment decision-making with machines for exploratory scenarios

AI/ML Solutions: Draw deep insights using intelligent machine learning services

We use our cross-domain experience to design innovative solutions for your business, meeting your objectives and the need for accelerating growth, improving efficiency, and moving from strength to strength. Our team of capable data scientists and solution architects leverage modern technologies cost-effectively to optimize resources and meet strategic imperatives.

Inquire Now to know more about our Databricks on AWS capabilities.

The post Building a Databricks Lakehouse on AWS to Manage AI and Analytics Workloads Better appeared first on Indium.

Being lightweight, fast, and providing increased accessibility to services are seeing the cloud growing rapidly. Globally, the spending on public cloud services is expected to grow 21.7%, from $396 billion in 2021 to $482 billion in 2022. By 2026, it is expected to be more than 45% of the enterprise IT spending as against 17% in 2021

The 10th annual Flexera 2021: State of the Cloud Report, based on a survey of 750 global cloud decision-makers and users on the use of public, private, and multi-cloud, reveals that:

● 92 percent of the enterprises have implemented a multi-cloud strategy, while 80 percent follow a hybrid cloud strategy

● 49 percent have silo workloads by cloud, and 45 percent integrate data between clouds

● Only 42 percent use multi-cloud management tools

A priority area for the organizations is to optimize the existing cloud deployment and migrate more workloads to the cloud. However, security, governance, and cloud management continue to pose a challenge for businesses following hybrid and multi-cloud strategies.

Challenges to Moving Workloads to the Cloud

The benefits of moving workloads to the cloud include

Scalability: Scaling up or down the workload based on demand fluctuations due to the unlimited availability of resources

Rapid Application Deployment: This is made possible as cloud servers, hosting environments, and serverless functions can be accessed as required

Distributed Infrastructure: Businesses can distribute their workloads across different geographies using the cloud, making IT resources resilient and fail-proof, ensuring business continuity even during disasters

Cost Efficiency: The pay-as-you-use model reduces the investments needed for IT infrastructure

These factors improve the ability of businesses to focus on innovation and delivering improved service to the customers, thereby increasing their competitiveness. However, there is also a disadvantage for every advantage that often sees businesses reverting their workloads to the on-prem systems. Some of the challenges include:

Spoilt for Choice: Businesses today have access to several cloud platforms. This is good news, of course, but it can also be confusing unless the IT team can assess the needs of the organization and find the right fit at the right price.

Application Compatibility: It is necessary to ensure that the applications being migrated to the cloud can run on the cloud. If not, they need to be refactored or even redesigned completely or partly to achieve the desired outcomes.

Resource Availability: Tools and strategies cannot be mapped directly between on-prem and cloud servers. They need to be redefined to suit the cloud requirements for management, security, and monitoring challenges. It also requires different skills for developers and the IT team.

7 Best Practices to Transition to the Cloud

To ensure the success of the workload migration process and experience the expected benefits, we at Indium Software recommend the following best practices. Indium offers cloud engineering services to optimize your investment in the cloud by offering reliable, agile, scalable, and secure solutions.

The best practices include:

Best Practice #1 Lead from the Front: It is important to get the organization’s buy-in for the migration effort to succeed. When the top management shows commitment to the process and takes the team along, it is easier to get the technical practitioners and other users also on board. Highlight the benefits, the challenges, and provide the necessary resources for the transition.

Best Practice #2 Identify the Best-fit Cloud Service: Brand names and reviews on the web may pull you in one or multiple directions. Either way, it can lead to confusion and failure. The best approach to decide on the right fit for your organization is to

– Do a cost-benefit analysis

– Evaluate the ease of deploying the workloads on the service

– Assess the monitoring and management capabilities of the service

– Estimate the security risks

Best Practice #3 Create a Roadmap: We can never have enough of a good thing and the temptation might be to do it all together, in one go. However, it is crucial to assess the effectiveness of an application in the cloud environment and decide whether it can be migrated or needs refactoring/reconfiguration/redesigning, or is best left behind. Also create a roadmap, with critical applications being uploaded first for minimizing disruption to the business.

Best Practice #4 Identify Potential Security Threats: The cloud is considered generally safer and the cloud service providers will have their own security processes in place. However, the threats still exist as any misconfiguration can leave a business’s applications vulnerable.

Therefore, putting the necessary security barriers around the business’s workloads will provide the necessary protection. This needs to be periodically reviewed and upgraded as well.

Best Practice # 5 Cloud Costing: Cost management is simpler in the cloud due to the pay-per-use model it uses. However, there may be other fees associated with the migration. Therefore, understand the pricing model clearly and identify methods to control costs.

Best Practice #6 Assign Roles: Assigning roles and elucidating responsibilities will create a sense of ownership to the migration process and improve the chances of its success.

Best Practice # 7 Plan a Cloud Roadmap: In a constantly evolving technology environment, it is essential to have a strategy that can help manage the uncertainties. While anticipating every change is not possible, a well-thought-out strategy will help provide the required direction.

You might be interested in: Cloud Data Migration Demystified

Indium has expertise in all major private/public cloud platforms and its team of experts can identify the right fit for your organization. Our services span:

● Cloud migration

● Modernization

● Optimization support for improved RoI

We ensure an optimal TCO after a seamless transition, providing the necessary tooling, automation, and experience for a safe and efficient migration to the cloud.

The post 5 Best Practices for Moving Data Workloads to the Cloud appeared first on Indium.

Indium Software, a cutting edge solution provider with cross-domain expertise, proposed transforming the manufacturer into a data-driven organization by migrating the data from on-prem databases to a cloud-based, serverless data warehouse.

The cloud-based data warehouse has become the need of the hour to keep the total cost of ownership (TCO) low while leveraging the services provided by the public cloud providers such as Google BigQuery, Amazon Redshift or Azure Synapse Analytics (Formerly SQL DW). In the case of the pump manufacturer, Indium migrated the client’s data to Microsoft Azure and reduced the TCO by over 50 percent.

This is the direction in which the world is moving today. According to a MarketsandMarkets report, the global serverless architecture market size will touch USD 21.1 billion by 2025 from USD 7.6 billion in 2020, growing at a Compound Annual Growth Rate (CAGR) of 22.7 per cent. The three key factors spurring this growth are:

• The need to shift from CAPEX to OPEX
• Remove the need to manage servers
• Reduce the infrastructure cost

Easy Data Access and Management

Today, data generated from multiple sources can be made available to businesses to improve their decision making and devising business strategies across functions. However, traditional systems cannot handle the multiple formats they are available in and manual intervention is required to reconcile them all into one format. This can be time-consuming and prone to errors.

A cloud-based serverless data warehouse can automate the process of data management, making data easily available and accessible for advanced analytics and to gain meaningful insights for improving business processes and efficiencies. Some of the key benefits of opting for a serverless data warehouse would be:

• Being cloud-based, it can be accessed from anywhere, thereby allowing even the executives on the move to access data and reports that can speed up their decision-making process.
• Being fully managed by the providers, it reduces the burden on the internal IT team and lets them focus on innovation and improving their core business
• A solution like Azure also enables easily scalable computational storage at lower costs. Databases can be paused and resumed quickly which can save costs. Cloud providers have a cost management feature to keep a check.
• The level of optimization it offers cannot be matched by the traditional on-premise setup
• It provides columnar storage and parallel processing facilitating faster aggregate queries
• High availability and scalability ensure automatic data distribution and replication automatically across data regions (zones) on the cloud infrastructure
• Data Latency is milliseconds despite a highly distributed data set up
• Data security is assured through authentication and authorization managed within the cloud setup and data encrypted to comply with privacy regulations

Challenges in Migration

Yes, opting for a serverless data warehouse is not a walk in the park. Some of the factors you must keep in mind include:

• Selecting the right building blocks is important as not all of them are fully managed. For instance, Amazon Redshift requires you to choose the node type that is compute-optimized or storage-optimized. You will need to choose the number of compute nodes for the cluster and also manually size them.
• In some instances, you might need to integrate different serverless building blocks and also connect the entire solution using non-serverless blocks.
• You may opt for integrating individual building blocks instead of having one single solution. While improving configurability it will make the solution complex.
• Depending on the data model you opt for, costs can be a combination of upfront and variable.

Partnering with the Right Data Experts

Navigating these hidden complexities requires a deep understanding of data, data warehouses as well as the service providers. An experienced solution provider such as Indium can work closely with you to understand your needs and tailor the approach to suit your requirements.

We provide a simple, secure, cost-effective and scalable solution. We have expertise in Data Modelling, the most crucial stage in architecting the data warehouse. We derive the Technology Architecture by analyzing the process architecture, business rules, metadata management, tools, specific needs and security considerations. At this stage, the data integration tools, data processing tools, network protocols, middleware, database management and related technologies are also factored in.

For the serverless data warehouse architecture, a step by step order of data pipeline and transformation from one form to another is done. As a result, the entire cycle of storage, retrieval, processing data within the data warehouse is mapped. The architecture is designed to ensure that the workload is processed on time, performance is optimized and running costs are kept low.

Indium has a team with more than two decades of experience in the latest cutting edge technologies as well as domain expertise across different industries such as retail, e-commerce, manufacturing, banking, services or finance, among others. If you would like to leverage serverless data warehouse for improved analytics and lower cost of ownership, do reach out to us.

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