If you weren’t able to join our recent Pingahla + Qlik session on Tariff & Supply Chain Risk Optimization webinar , no worries. You can catch the full recording here. Here’s a quick walkthrough of what we covered so you can decide if it’s worth sharing with your finance, supply chain, and procurement teams (spoiler: it is) . Why We Built a Tariff & FX Optimization Solution I kicked things off by framing the problem many manufacturers, CPG brands, and global supply chain organizations are living with every day: Tariffs and FX rates are volatile and politically driven—but your margins and pricing can’t be. Most teams are reactive: they get a static report after  the damage is done. Limited visibility across suppliers, countries, lanes, and FX sources makes it hard to answer simple questions like: “What’s our tariff exposure by lane or product family?” “What happens to contribution margin if FX moves 3%?” “Where can we reroute or re-source to protect margins?” To address that, Pingahla partnered with Qlik  to build an end-to-end, production-ready solution  that: Ingests  data from ERPs, procurement systems, freight forwarders, trade intelligence platforms, and FX feeds using Qlik Talend Cloud Cleans and reconciles  it using our Pingahla Qlik Talend AI Data Quality Accelerator (PDQA) Delivers analytics, what-if scenarios, and optimization recommendations  with Qlik Cloud Analytics , including AI and predictive modelling The Architecture in Plain English Before handing it off to Luis for the live demo, we walked through the high-level architecture: Data Sources ERPs: SAP, Dynamics, Infor, and others Procurement & freight: TMS / freight forwarders, logistics platforms FX & macro: IMF, ECB, and other FX feeds Government HTS / tariff APIs  for near-real-time updates Optional third-party trade intelligence (e.g., Import Genius, S&P Global, etc.) Integration & Transformation – Qlik Talend Cloud Extracts data from all these systems (APIs, files, DBs, message queues) Transforms and harmonizes it into a unified model Applies business rules for tariffs, lanes, materials, and suppliers Data Quality – PDQA Detects duplicates, anomalies, and conflicts across multiple sources (e.g., four FX feeds, one outlier) Surfaces trusted “golden records” for key metrics like FX, tariffs, and costs Ensures dashboards and models are built on validated, explainable data Analytics & Optimization – Qlik Cloud Interactive dashboards and visualizations for business users AI-driven insights and predictive scenarios Optimization recommendations for reroutes, alternate suppliers, and lane selection We designed it to be proactive , not just another reporting layer that tells you what went wrong yesterday. Live Demo: Inside the Tariff & Supply Chain Risk Dashboard Our Qlik Solution Architect, Luis Alejandro Bernal , then walked through the actual dashboard we built for a global paint manufacturing company . He showed four main tabs: 1. Executive Overview & Risk Snapshot At the top: filters like currency, shipment date, product family, and lane , so leaders can slice exposure instantly. Key KPIs included: Total tariff cost Tariff as a % of COGS FX impact at risk On-time delivery rate Contribution margin delta vs. baseline Visuals included: Tariff cost impact on contribution margin over time Top sourcing countries by COGS exposure FX exposure by currency pair The most actionable piece on this page: Lane Optimization – Recommended Reroutes Shows current lane  vs optimized lane Includes rationale (e.g., better cost, lower risk, improved reliability) Can optimize for revenue, cost, time, or a custom business objective Alerts & Exceptions HTS updates Currency alerts Other rules-based notifications that matter to trade, finance, and supply chain teams 2. Raw Material Price & Volatility Analysis Here Luis showed how the solution helps teams manage raw material price risk : Cost of materials over time Price volatility index Tariff impact and FX-adjusted cost High-risk materials  as a % of total Key visuals: Cost share by country  (China, India, US, and others) Raw material price trends & volatility , helping teams choose strategic buy/sell windows 3. FX Monitoring & Exposure This tab focused on FX risk  and how it evolves: Currency FX rate over time Average exchange rate % Volatility index % Rate spread and days in range By selecting a target currency (e.g., CNY), users can: See historical behavior and volatility Identify peaks and troughs Use that insight when layering tariffs and lane decisions on top 4. Supply Chain Risk & At-Risk Suppliers The final tab zoomed out to the full supply chain picture : Total trade value Tariff % Tariff-adjusted value Cost uplift Top at-risk suppliers The standout visualization here was the Sankey diagram : Flows from supplier → origin country → material → destination region → product family Line thickness represents trade value moving through each node Makes it easy to see where risk and value are concentrated in the network This gives a powerful “single pane of glass” view of how tariffs, FX, and sourcing decisions intersect  across the global supply chain. Audience Q&A Highlights We wrapped with a robust Q&A. Here are some of the themes that came up: How often do you update tariffs and FX rates? The model supports scheduled and automated refreshes : As often as every 5–15 minutes  for most sources Real-time  is also possible when the source supports streaming, webhooks, or message queues Can we integrate our own HTS, FX, and trade data sources? Yes. Using Qlik Talend Cloud , we can ingest from: Government tariff APIs Custom HTS files IMF / ECB FX feeds Trade intelligence platforms (e.g., Import Genius, S&P Global) Any third-party API or export your team uses today Everything is configurable to your business rules . Do you support what-if scenarios and hypothetical tariff changes? Yes. The solution includes a scenario engine  where users can adjust: Unit tariff rates FX markups Duty percentages …and see the impact on COGS and contribution margin , comparing baseline vs. adjusted  scenarios before making procurement decisions. Our ERP is heavily customized. How fast can you onboard us? Qlik Talend is built for complex, custom ERPs: Supports 2,000+ connectors (APIs, databases, file formats, etc.) Typical ingestion and mapping can be done in days, not months PDQA further accelerates onboarding by automating data quality checks and surfacing trusted fields Is this self-service for business users, or IT-locked? The dashboards are designed for business teams : Drill from executive KPIs down to shipment-level detail Interact with associative filters Export data Build new visualizations (depending on permissions) Finance, procurement, and supply chain users can get answers without waiting on IT . Is optimization rules-based or machine-learning driven? It can be either or both : Out-of-the-box:  A rules-based framework using historical performance, transportation costs, and lane efficiency Advanced:  ML models predicting lane reliability, delays, or optimal routing, leveraging Snowflake, Databricks, or Qlik AutoML Customers can start simple and evolve toward more advanced ML-driven optimization over time.

In today’s business landscape, organizations are expected to do more than deliver services. They must also meet stringent data security and regulatory requirements. At Pingahla, we see quality and compliance as inseparable, and balancing them is central to building trust with our clients. How We Deliver Quality Quality is never a one-time goal for us. It’s a continuous practice, built on: Robust processes  that bring consistency and precision to every project Continuous improvement  through lessons learned and new innovations Client feedback loops  that ensure we always stay aligned with business needs Team accountability  so every deliverable reflects the highest standards This approach ensures projects are not just delivered, but delivered right. Compliance Built Into Everyday Work Compliance at Pingahla is not a checklist; it’s a culture. Our framework covers: Data classification and retention policies  that safeguard sensitive information ISMS practices  for a structured approach to information security Regular audits and assessments  to keep standards in check Employee training and awareness  so that compliance is second nature to every team member Together, these measures reduce risks and assure clients that their data is always secure. Real-World Impact Across Industries Our integrated approach has made a measurable difference for clients across sectors: Financial Services : While migrating sensitive customer data, we balanced speed and accuracy with strict regulatory standards, ensuring the client could move forward without compliance concerns. Healthcare : We implemented strong data governance practices during a cloud migration for a healthcare provider, protecting patient information while enabling faster analytics. Retail and E-commerce : When helping retailers consolidate large volumes of sales data, we built quality checks into the process while ensuring compliance with GDPR and other data privacy regulations. These examples highlight how we adapt to client needs while keeping quality and compliance at the forefront. Why This Matters What sets Pingahla apart is our ability to bring these priorities together. We don’t see compliance as a burden or quality as a finish line. We treat them as ongoing commitments that define how we work and interact with clients. This proactive mindset allows us to: Spot risks early and resolve them before they escalate Deliver solutions that withstand both market expectations and regulatory scrutiny Build long-term partnerships based on trust, not just transactions A Trusted Partner for the Future By embedding quality and compliance into our DNA, Pingahla positions itself not just as a service provider but as a trusted partner. As regulations grow stricter and client expectations rise, we remain committed to strengthening our practices so that trust, security, and excellence continue to define every client relationship.

In today’s rapidly evolving data-driven world, managing data effectively is no longer optional, it's essential. As organizations continue to generate massive volumes of data across systems and channels, the need for reliable, accurate, and accessible information becomes critical. This is where Master Data Management (MDM) steps in as a foundational element of any successful data strategy.   The Power of Data in a Modern Enterprise Data plays a vital role in empowering organizations to make informed decisions, solve complex problems, and uncover valuable insights. From understanding customer behavior to optimizing operations and enhancing user experiences, data fuels innovation and growth across all business functions. A robust data foundation enables cross-functional teams to collaborate efficiently, eliminate silos, and foster a data-driven culture. In today’s customer-centric economy where growth is directly tied to the quality of customer experience having real-time, reliable customer data is a competitive necessity. Missed opportunities, fragmented interactions, and inaccurate profiles can significantly hinder business success. What is Master Data Management (MDM) ? Master Data Management is the process of consistently and accurately identifying and managing core business entities such as customers, suppliers, products, employees, and locations across various systems and touchpoints. It involves consolidating entity data from multiple sources to create a single, unified, and trusted view of the business-critical information that drives decisions. Master data is the essential, mission-critical information that organizations depend on. It provides the foundation for business transactions, customer interactions, and analytics. Examples include: People  :  Customers, suppliers, employees Places  : Locations, branches, warehouses Things  : Products, assets, inventory Without effective MDM, businesses often face issues like duplicate records, data inconsistencies, inaccurate reporting, and inefficient processes. The Challenge with Unmastered Data Take an instance of a life sciences industry, where data is collected across a multitude of channels - patient portals, healthcare providers (HCP) interactions, clinical trials, digital health apps, electronic medical records (EMRs) and more. Each of these touchpoints may capture information related to patients, physicians, trial participants or healthcare institutions  in different systems and formats. Now, imagine a single healthcare professional engaging with a life sciences company across various touchpoints like attending a virtual event, prescribing a therapy, participating in a clinical trial etc, without master data management in place each interaction might be captured in a different system often resulting in duplicate or inconsistent records for the same HCP. This data fragmentation can cause several issues like  : Multiple conflicting profiles for the same HCP. Uncoordinated outreach from different departments. Missed opportunities for personalized engagement. Risk of non compliance with regulations like GDPR. With MDM, all of these disparate data points are : Matched and Merged using business rules. De-duplicated to ensure a single, golden HHCP profile. Enriched with the validated data from external data providers. Governed through stewardship and compliance controls. Thus, the result is a 360 degree unified view of HCP that is uniformly accessible across departments. By ensuring a single source of truth for master data, MDM enhances every strategic initiative from marketing personalization and customer engagement to operational efficiency and digital transformation. Elevating data with the help of MDM. In my role as the lead for Master Data Management (MDM) implementation at a leading pharmaceutical and life sciences company, I witnessed firsthand how an effective MDM system can drive significant business value. The organization experienced tangible improvements in operational efficiency and stakeholder engagement through the implementation of a robust MDM solution. The business initially faced substantial challenges due to duplicate healthcare professional (HCP) profiles originating from multiple touchpoints and residing in a centralized data lake. These inconsistencies led to increased costs in marketing campaigns, inaccurate field representative targeting, and incorrect payouts to HCPs participating in seminars, conferences, and guest lectures. Throughout the engagement, I collaborated closely with business stakeholders, product owners, and end users to design and implement a comprehensive MDM solution that addressed these pain points. The result was a transformative system that improved data quality and usability for analytical and operational purposes. The highlights of the implemented MDM system includes A 360-degree view of HCP profiles by integrating data from multiple sources using advanced match and merge logic with source-specific survivorship rules. A fully customized user interface that enabled business users to view and update data efficiently. Multi-level approval workflows for data change requests, ensuring strong data governance aligned with user roles. Role-based security to control data access and maintain compliance. A tailored workflow to accurately determine HCP expert levels, ensuring correct payouts. Improved data quality through integration with third-party providers for address, phone, and email validation. The outcome has been substantially improved as the business saw  Reduced marketing campaign costs by eliminating duplicate HCP records. Accurate and fair HCP payouts for business events based on verified expert levels. Enhanced operational efficiency driven by a unified and reliable 360-degree HCP profile. Increased response rates from mail campaigns due to improved data accuracy. Master Data Management isn’t just about cleaning data, it’s about transforming it into a strategic asset that supports the entire business. MDM empowers organizations to build accurate, real time and holistic views of their key entities.  How does Pingahla empower growth through Master Data Management? At Pingahla, we specialize in building robust and scalable Master Data Management (MDM) solutions that help organizations turn fragmented data into a trusted, unified asset. In today’s complex digital landscape, where data is generated across countless systems and channels, businesses face an uphill battle to maintain data consistency, accuracy, and accessibility. Our MDM solutions are designed to  Eliminate duplicate and inconsistent data across departments and platforms. Unify data related to Customer, Products, Suppliers and more into a single version of truth. Enhance personalized experiences by delivering a 360 degree view of business entities. Enhance decision making with high quality, real time data. Support regulatory compliance and data governance best practices. Whether you're in life sciences, retail, manufacturing, or financial services, Pingahla’s MDM frameworks are tailored to meet industry-specific requirements and integrate seamlessly into your existing ecosystem.

Transforming EDI Complexity In today’s fast-paced digital landscape, managing complex Electronic Data Interchange (EDI) workflows in cloud ecosystems such as Informatica’s Intelligent Data Management Cloud (IDMC)  is critical for ensuring operational agility and business continuity The Challenge: Legacy EDI Code Slowing Progress A forward-thinking enterprise was experiencing performance and maintenance challenges within its EDI transaction flows, both inbound and outbound, executed through IDMC’s B2B Gateway . The system was burdened by: Legacy PowerCenter DT (Data Transformation) code in outbound mappings High complexity and effort required for minor updates Slowed business agility and increased risk of errors Realizing the need for modernization, the customer partnered with Pingahla  to streamline and scale its EDI processing. Pingahla’s Strategic Approach to EDI Modernization Pingahla conducted a deep dive assessment of the client’s existing IDMC and EDI implementation. The objectives were: Optimize EDI Handling:  Improve the efficiency of EDI message processing in IDMC’s B2B Gateway Simplify Maintenance:  Migrate complex DT code into more manageable structures Enable Scalability:  Deploy a flexible, reusable framework tailored to the client’s EDI endpoints and business logic Key Solution Highlights ✅ 1. Eliminated Legacy DT Code Pingahla reverse-engineered PowerCenter DT logic embedded in outbound mappings to understand business rules. These rules were then re-implemented using IDMC-native tools. ✅ 2. Migrated to IDMC Hierarchy Mapper All business logic was transitioned to Informatica's Hierarchy Mapper , ideal for handling complex, structured data formats like EDI. The benefits: Easier updates and scalability Clear separation of data extraction and transformation logic Reduced dependency on legacy ETL code Sample Hierarchy Mapper View Above is an image of the implemented Hierarchy Mapper  used in the transformation process What is a Hierarchy Mapper  A Hierarchy Mapper allows users to map data from hierarchical schemas (like XML, EDI, or JSON) to a structured target schema. It is a native component of IDMC that improves data transformation performance and manageability.   Read more from Informatica ✅ 3. Introduced Two-Phase Outbound Processing Pingahla separated the outbound process into two modular phases: Phase 1:  Extract ERP data and generate intermediate XML at a designated server location Phase 2:  Use Hierarchy Mapper to convert XML to the final EDI format This modularization added transparency and simplified debugging. ✅ 4. Built-In Event Tracking for Visibility An event tracking table  was introduced to monitor transaction progress: Tracks each step of the data transformation pipeline Captures errors with descriptive messages Enables faster troubleshooting and error resolution Business Outcomes: Simplicity Meets Scalability By adopting Pingahla’s IDMC EDI Framework, the customer experienced: Reduced Maintenance Overhead:  Easier to manage and extend Enhanced Scalability:  Add new message types and rules without disrupting existing flows Improved Observability:  Real-time monitoring of transactions and error logging Better Alignment:  Seamless integration with evolving business systems and endpoints Conclusion: From Complexity to Capability This successful transformation underscores how Pingahla unlocks the power of data by delivering secure, scalable solutions in cloud, on-prem, or hybrid environments . Our approach to EDI modernization using Informatica IDMC  enabled the customer to break free from legacy constraints and achieve operational excellence. Ready to Modernize Your EDI Workflows? Let Pingahla simplify your EDI challenges in IDMC. Contact us  to learn how we can help you streamline and future-proof your data integration.

More and more of Pingahla customers realize and see the benefits of Talend Cloud and have started to migrate and move to Talend Cloud as well as current customers just upgrading to the latest on-prem platform. But before making this jump to Talend Cloud or utilizing new features, you have to make sure you are at least on version 7.x. The following is due to prior versions with Talend being the end of life and as well as having Talend Cloud support the data pipelines that have been built with the latest version of Talend Studio. But where and how should you start? This is the million-dollar questions many of our customers ask, so I thought it would be great to share insights if you are a Talend customer either moving to Talend Cloud or upgrading to the latest version of Talend. Note: This blog post will focus on Talend main integration solution and not its sub solutions such as Data Stewardship or Data Preparation, which would also need to be upgraded Before you get started with any type of upgrade, the first phase would be INVENTORY . First document and identify the number of data pipelines you have built with the studio, types of components/connectors being used, types of connections, and are the jobs built batch or real-time. This phase is important as many components/connectors in prior versions are either deprecated and replaced with new and improved components/connectors. Also, if you are using any connectors from the community, they may no longer work in which you will need to download the latest version if one is available. Here is a listed of deprecated components/connectors for Talend Enterprise Solutions; Talend Data Integration Solution Talend Big Data Solution Talend Data Management Solution Talend Big Data Platform Solution Talend Data Services (ESB) Solution Talend Data Fabric Solution Now that you have documented and defined your Talend inventory for your upgrade the next main question I have for you is that will you be upgrading to Talend Cloud, or will you stay with Talend’s on-prem solution? The reason I ask you this the question, because when upgrading to the latest Talend on-prem solution, in addition, to upgrading the Talend Studio jobs, you will also need to update Talend Administrator Console (TAC) along with its other components such as job servers, etc. But before we get into that, your next major step will need to be BACKUP . Backing up the environment and is key, and spending on your current version, Talend has done a great job in documenting the following steps. https://help.qlik.com/talend/en-US/migration-upgrade-guide/7.3/backing-up-environment Once you have completed your back up comes the phone part. Now upgrading Talend will not be as easy as 1, 2, 3. Depending on your current version, the following can be painful. Why so? If you are on still on version 5.x you won’t be able to easily move to 7.3 Talend’s latest version as of 2021. For you to move to the latest you will need to upgrade in iterations ( PHASED APPROACH ) along with possibly recreating and updating your pipelines. For this major step, I highlight recommend reaching out to your preferred Talend expert, Talend Professional services, or Pingahla :). But without giving up too much of Pingahla’s secret sauce, let me provide some high-level details. CONTACT your Talend Support and let them know you are planning an upgrade. This is important as your support team will need to issue you a temp license based on the different versions. Due to this, getting new license keys can take anywhere from one week, we have seen. Please note: After the upgrade you will need to notify support to obtain a definitive license. Only do this once the upgrade is complete and everything is working as is, as requesting a definitive license for an upgrade can affect your current envoriment. Don’t upgrade all environments at the same time ! We had a customer request the following in which we advise was a bad decision fortune for us, they did listen. Start with a Sandbox or DEV to upgrade first. This will allow you to make sure the approach you are taking, along with the replacement of components/connectors, updates to pipelines, and servers, won’t have any impacts. DOWNLOAD all necessary files before starting your upgrade. You would be surprised how simple this step is that it is often missed. If you notify your Talend Support, they will be emailing you the necessary files for download . The upgrade will need to be a phased approach . For example, if you are on version 6.4, don’t just jump to version 7.3. UNDERSTAND what are the major releases in each version. For example, if you are on version 6.4, Pingahla would recommend moving to 7.0, then to 7.3. But again, it’s just not that easy. Understand what components/connectors need to be replaced, what new jobs will need to be updated, what new patches need to be applied to servers if using on-prem. These are just some of the things to keep in mind. TEST , test, and test! It is critical to test the major updates in each phase of the upgrade. Waiting until you complete the upgrade to the latest version and testing will cause you major delays and headaches. When upgrading besides from data pipelines, not working, other key aspects you would want to look out for is; Security – Validating security with connections, Talend users, etc Run-Times – Are the data pipelines running at the prior data load speeds and have or faster? This should not be slower. Data Counts – Making sure data counts are the same, and data is not being dropped. Plan go-live. With any major upgrade its important to let all the dependent processes and users that could be impacted. · NOTIFY. Once the upgrade is completed, it is important to notify your Talend Support to let them know your upgrade is complete. During this time, the temp licenses they provided will be invalid, and you will now have a new key for the latest and greatest version. If you need support in upgrading your Talend solution, please reach out to sales@pingahla.com as we discuss how we can support and get you onto the latest version of Talend.

Five months since the release of Talend 8.0 and Pingahla is supporting its Talend customers moving to Talend 8. Now if you are a Talend Cloud or On-prem customer you may have missed the release or if you are a Talend Cloud customer, you maybe ask yourself, " Why do I need to upgrade on the cloud? " No matter the type of Talend solution you are using today you will most likely upgrade to Talend's latest version 8.0 for ongoing support and to also take advantage of the latest and great updates. But let's start with the 8.0 updates for both on-prem and cloud solutions. Talend On-Prem Solutions Talend's On-Prem solutions from Data Integration, Application Integration (ESB), Big Data and etc have included some new changes the include; New Features such as; Popups about changes not pushed to Git remote repository Support of Spark Universal 3.1.x in Local mode Improvement of error messages for Git operations Support of Databricks 8.0 and onwards with interactive clusters only on Spark Universal 3.1.x as a technical preview Bug fixes with its products such as Data Integration, Data Mapper, Data Quality, Data Preparation, and Data Stewardship. Deprecated and removed items Bonita BPM Integration Talend CommandLine as a server MapReduce Oozie For a full list of understanding the latest on-prem updates, please visit; Talend Data Integration - https://help.qlik.com/talend/en-us/release-notes/8.0/r2022-07-studio Talend Big Data - https://help.qlik.com/talend/en-us/release-notes/8.0/big-data-new-features Talend Application Integration - https://help.qlik.com/talend/en-US/release-notes/8.0/esb-new-features Talend Cloud Solutions Talend's Cloud platforms starting from Talend Cloud Data Integration to Talend Data Fabric included many new updates such as; Updates to the Talend Cloud Migration Platform New page to manage logs New task logging architecture SSO settings for multi-tenancy Public API versions Along with some bug fixes such as; Names of Remote Engines in a cluster were not displayed Timestamps had incorrect format when using API to execute plans Deployment Strategy was wrong in the TMC API To better understand the full list of updates, enchantments, and feature depreciations, please visit https://help.qlik.com/talend/en-US/release-notes/8.0/about-talend-release-notes These updates, enchantments, and feature depreciations are critical to understanding as they could have major impacts on current data pipelines within your organization, or help easily support new data initiatives. If you are interested in better understanding how the latest updates, enchantments, and feature depreciations and how they can affect your Talend ecosystem, contact sales@pingahla.com for an assessment to allow our Pingahla team to put together a delivery plan in place to utilize the latest and great version of Talend.

In today's data-driven world, companies must constantly adapt to keep pace with the rapid evolution of technology. As businesses grow and their data needs become more complex, the choice of data platform can significantly impact their ability to scale, innovate, and drive value from their data. While Cloudera has long been a trusted platform for extensive data management, many organizations now find that it no longer meets their evolving needs. This is where Databricks comes into play. Databricks, a unified data analytics platform, is rapidly becoming the go-to solution for companies looking to maximize the potential of their data. Here's why switching from Cloudera to Databricks is the best move for your company. Unparalleled Scalability and Performance Cloudera's architecture, while powerful, can become cumbersome and difficult to scale as data volumes increase. Databricks, on the other hand, is built on Apache Spark and designed to handle massive datasets easily. Its scalable architecture allows you to process data more efficiently, enabling faster insights and reducing the time to value. Databricks also offer superior performance in data processing. It seamlessly integrates with cloud environments like AWS, Azure, and Google Cloud, leveraging cloud computing power to optimize resource usage and minimize costs. This means your data teams can run complex workflows faster and more cost-effectively than ever before. Unified Platform for Data Engineering, Machine Learning, and Analytics One of Databricks' key advantages is its ability to unify data engineering, machine learning, and analytics on a single platform. Cloudera often requires multiple disparate tools and systems to achieve the same goals, leading to inefficiencies and increased complexity. Databricks simplifies this by offering an integrated environment where data engineers, scientists, and analysts can collaborate seamlessly. This unified approach not only streamlines workflows but also fosters innovation, as teams can easily share data, models, and insights without complex integrations. Enhanced Data Quality and Governance Data quality and governance are critical for any organization. Cloudera's traditional architecture can make implementing robust data quality and governance frameworks challenging, especially as data environments become more distributed. Databricks addresses these challenges head-on with its powerful data management capabilities. It offers built-in tools for data quality checks, automated data lineage tracking, and compliance monitoring. These features ensure that your data is accurate, reliable, and compliant with industry regulations, giving you confidence in your data-driven decisions. Cost Efficiency and Flexibility Managing and maintaining a Cloudera environment can be costly, mainly as your data needs grow. Licensing fees, infrastructure costs, and the need for specialized skills can quickly add up, making it difficult to control your total cost of ownership. Databricks offers a more cost-effective solution. Its cloud-native architecture allows you to pay for only what you use, with the flexibility to scale resources up or down based on demand. This reduces costs and provides greater agility, allowing your organization to adapt quickly to changing business needs. Seamless Migration with Pingahla's Cloudera to Databricks Accelerator While the benefits of moving to Databricks are clear, the migration process can seem daunting. This is where Pingahla's Cloudera to Databricks Accelerator comes in. Our accelerator is designed to simplify the migration process, ensuring a smooth and efficient transition with minimal disruption to your business operations. Our team of experts will work closely with you to assess your current Cloudera environment, develop a customized migration plan, and execute the migration seamlessly. With our proven methodologies and automated tools, we can help you unlock the full potential of Databricks quickly and efficiently. A Future-Ready Data Platform The shift from Cloudera to Databricks is more than just a technological upgrade—it's a strategic move that positions your company for future success. With Databricks, you gain access to a robust, scalable, and cost-efficient platform that enables you to harness the total value of your data. If you're ready to take your data strategy to the next level, now is the time to consider switching to Databricks. With the support of Pingahla's Cloudera to Databricks Accelerator, your journey to a more agile, innovative, and data-driven future is within reach. Pingahla's Cloudera to Databricks Accelerator.pdf

Introduction: EC2 Instances serve as the fundamental building blocks of your cloud setup—crucial for your virtual environment. But here's the challenge: keeping costs under control can be tricky, especially when you want to make sure you're not overspending. If you need an environment for ad-hoc work, you know they don’t need to run constantly. Why pay for server uptime while you’re off the clock? You might want to turn these instances on and off according to your needs. So, what are your choices? You want to avoid labor-intensive and inefficient approaches. And you obviously cannot keep your EC2 instances running 24/7 unless you're using a free-tier instance. Automate the Process  – Here’s the exciting part! You can automate the start and stop times of your EC2 Instances using a few AWS services. By the end of this article, you'll have a streamlined, cost-efficient way to manage your EC2 Instances effortlessly. Let’s dive in! Purpose: It is easy to spin up a new EC2 instance, and along with the convenience comes the price. On-demand user pricing is charged per hour, which can be hard on you if you are not careful with start/stop. Below are 2 scenarios discussed from a user’s perspective and their solution. Scenario 1 A user logout time is unknown, while the EC2 instances are left running unintentionally.   Solution: The CloudWatch alarm monitors metrics and sends notifications to an SNS topic, triggering a Lambda function to stop the instance. This setup helps automate resource management based on specified conditions. This solution is less discussed as it involves identifying appropriate CPU utilization percentages that can be regarded as idle/inactive EC2 instances. Steps: 1)Create an IAM policy and IAM role for your Lambda function a)Create IAM policy i)Set permissions for EC2. ii)Select Write Access level: StartInstances, StopInstances. iii)Specify resource ARNs: Add ARNs and mention resource region, resource instance (instance Id and resource instance (instance ID). b)Create IAM role i)Choose the above policy while creating the role. 2)Create lambda functions to stop EC2 a)Create function: Author from scratch. b)Choose Python 3.9 for runtime. c)Choose an existing role and choose the IAM role created above. d)Under the create function on the code tab, use the code below and update the region and instance IDs. import boto3 region = '' instances = ['', ''] ec2 = boto3.client('ec2', region_name=region)   def lambda_handler(event, context):     ec2.stop_instances(InstanceIds=instances)     print('stopped your instances: ' + str(instances)) e)Deploy code and test the function. 3)Create an SNS topic a)Configure the topic and choose the Standard option. b)Create a subscription and select the lambda created as the endpoint. 4)Create a Cloudwatch alarm a)Choose the appropriate metric that you want to monitor. Such as CPU utilization for the instance < ~10% for 1 hour (appropriate % is not confirmed). b)Select the instance metric on which you want to base the alarm and define the threshold conditions. c)Configure alarm to send a notification to the SNS topic created earlier. d)Review, provide a name for the alarm, and create.   Scenario 2 When the EC2 instances are left running, the user logout time is known .   Solution: The below setup allows you to automate the stopping of instances based on schedules or specific events, reducing manual intervention and optimizing resource management. This solution is quite common and is discussed in a few blog posts.     Steps: 1)Create an IAM policy and IAM role for your Lambda function a)Create IAM policy i)Set permissions for EC2. ii)Select Write Access level: StartInstances, StopInstances. iii)Specify resource ARNs: Add ARNs and mention resource region and resource instance (instance ID). b)Create IAM role i)Choose the above policy while creating the role. 2)Create lambda functions to stop EC2 a)Create function: Author from scratch. b)Choose Python 3.9 for runtime. c)Choose an existing role and choose the IAM role created above. d)Under the create function on the code tab, use the code below and update the region and instance IDs. import boto3 region = '' instances = ['', ''] ec2 = boto3.client('ec2', region_name=region)   def lambda_handler(event, context):     ec2.stop_instances(InstanceIds=instances)     print('stopped your instances: ' + str(instances)) e)Deploy code and test the function. 3) Create EventBridge rules that run your Lambda functions a)Create rule on the console. b)Choose Schedule on rule type. c)Under the schedule pattern, choose the recurring schedule and the Cron-based schedule. d)Select Minute, Hours, Day of month, Month, Day of the weekthe month, Month, Day of the week, and Year.  e)Select targets, choose Lambda function from the Target dropdown list, and finally create.   Conclusion: By leveraging these techniques, you can shift your focus back to what truly matters—your core work—while leaving the manual management of EC2 instances behind. This streamlined setup boosts both efficiency and cost-effectiveness, automating your instance operations effortlessly. Dive into the perks of automated management and enjoy the significant cost savings it brings to your AWS environment.   Reference:   https://repost.aws/knowledge-center/start-stop-lambda-eventbridge

Introduction Applications created and built to use cloud computing platforms are known as “cloud-native” applications. Cloud-native testing is a specialized approach to software testing that focuses on applications and services designed for cloud-native architectures. It includes testing of microservices, orchestration tools, and other cloud-specific components. Cloud-native testing includes various types of testing, such as unit, integration, security, performance, and scalability. It plays a crucial role in phases of the software development lifecycle. Differences between Traditional Testing and Cloud-Native Testing Environment: Traditional testing often occurs in controlled, static environments, while cloud-native testing is designed for dynamic and scalable cloud environments. Scope: While traditional testing usually concentrates on large systems, cloud-native testing uses orchestration tools and microservices. Automation: Cloud-native testing heavily relies on automation to test frequently changing cloud-native components, whereas traditional testing may involve more manual processes. Scalability: Cloud-native testing involves testing for scalability and resilience in response to fluctuating workloads. Traditional testing does not address this feature. Tools: Cloud-native testing often requires specialized tools designed for the cloud-native ecosystem, whereas traditional testing uses more traditional testing terminologies. Security: Security testing in cloud-native applications must address data container vulnerabilities and cloud-specific security concerns, which are less important in traditional testing. Dynamic Nature: Cloud-native testing must adapt to the dynamic nature of microservices and orchestration, while traditional testing deals with more static application structures. Objectives of Cloud-Native Testing The primary goals and objectives of cloud-native testing include - Reliability: Ensure the reliability and stability of cloud-native applications, especially in dynamic and distributed environments. Performance: Verify that applications can handle varying workloads efficiently and without decrement in performance. Security: Identify vulnerabilities and security weaknesses specific to cloud-native components, including data containers and microservices. Scalability: Testing the application's ability to scale up or down to meet changing demands effectively. Compatibility: Ensure our cloud-native application works seamlessly across various cloud providers and platforms. Continuous Feedback: Provide ongoing feedback to developers and operations teams to improve the application continuously. Compliance: Validate that the application complies with industry standards and regulations, especially when handling sensitive data. Cost Efficiency: Ensure that the application's resource utilization is optimized over time to minimize cloud infrastructure costs and IPU consumption. Automation: Implement automated testing processes to keep pace with frequent code changes and deployments in a cloud-native environment. Key Advantages Of Cloud-Native Applications Agility: Cloud-native applications make rapid development, deployment, and iteration possible. With the help of infrastructure, developers can easily bundle and deliver new features or bug fixes. Businesses may react to market changes more quickly, publish updates more frequently, and gain a competitive advantage. Cost-effectiveness: Cloud-native applications maximize resource use by scaling up or down in response to real demand. Thanks to elastic scaling, organizations can distribute resources as needed, avoiding the needless costs associated with over-provisioning. Additionally, cloud-native architectures lessen the need for expenditures in on-premises infrastructure by utilizing cloud provider services. Better management: Testing cloud-native applications also helps to simplify infrastructure management, which is an additional advantage. Serverless platforms such as AWS and Azure have eliminated the need for businesses to worry about things like allocating storage, establishing networking, or provisioning cloud instances. Collaboration and Communication: Cloud-native testing promotes teamwork and communication among development, testing, and operations teams. Effective communication channels and collaborative tools aid in the timely sharing of test plans and results and the resolution of concerns. Automation and Continuous Testing: Test automation and continuous testing are the main focus of cloud-native testing. Because automated tests can be run quickly and often, every change can be completely tested before being pushed to production. Cloud-native applications are designed to withstand failures. Owing to its distributed architecture, the application can function even in the event of a failure in one of its services, providing a higher degree of fault tolerance and lessening the effect of failures on the system as a whole. Scalability: Scalability is a huge advantage of testing cloud-native applications. Cloud-native applications are designed to scale with ease. Applications can dynamically distribute resources based on demand by leveraging containerization and orchestration platforms. This allows programs to function at their best by efficiently handling different workloads. Flexibility and Portability: Cloud-native applications are platform-independent. They can be implemented on a variety of cloud providers or even on-premises. Because of this flexibility, businesses can choose the cloud provider that best meets their requirements and even switch providers as needed. Cloud-native applications are now exploding in the tech industry. Considering its vast benefits, most enterprises are moving towards the cloud as fast as possible. Common Challenges in Cloud-Native Testing Testing Serverless Functions: It might be difficult to precisely estimate and evaluate response times for serverless functions because they sometimes have varied cold start periods. Since the local environment frequently varies greatly from the cloud, testing serverless functions locally can be challenging. Handling Stateless programs: Testing becomes more difficult because stateless programs rely on other databases or services for data storage. Testers must consider the application's statelessness to ensure that each request may be handled separately. Complex Interactions Among Microservices: When there is asynchronous communication, it can be difficult to coordinate testing across several microservices. It might also be difficult to confirm that microservices operate in a union since modifications made to one service may impact others. Diverse Cloud Environments: Vendor Lock-In—Because every cloud provider offers different features and services, it might be challenging to guarantee cross-platform compatibility. Service Dependencies—Testing can become challenging when an application uses third-party APIs or various cloud services. Best Practices for Efficacious Cloud-Native Testing Shift Left: Perform testing as soon as possible during the development phase to identify problems early and lower the cost of addressing them later. Leverage Automation: Invest in automated testing to stay up with rapid deployments and changes in cloud-native settings. Make consistent use of infrastructure as code while establishing test environments. Chaos Engineering: Utilize chaos engineering to find weak points in your system and ensure it can fail gracefully. To continuously increase system resilience, conduct chaos experiments regularly. Monitor and Observe: To acquire knowledge about the performance and behavior of applications and implement strong monitoring and observability procedures.

We all know that organizations are making huge investments in Artificial Intelligence and Machine learning (AI/ML). While that is being done data-driven enterprises ought to know that data is an asset as bad data would drive bad decisions and models. You need some form of Data Governance to drive effective business insights and innovation. Organizations today face several challenges related to data quality and poor data management. Fraud and security breaches are one of their topmost concerns and this is where the data needs to be managed and governed efficiently and Data governance comes into play. An organization meticulously takes care of its inventory, suppliers, finances, and employees. And that is the same way that enterprise data needs to be treated. What is Data Governance? Data Governance is a set of different rules, policies, processes, and standards that manage the availability, security, and quality of data within an enterprise system. Resolving data inconsistencies would be a task if not for data governance. For instance, if a customer’s address is different in person, inventory, and sales systems then it could mess up the data integration efforts. This will not only cause issues in data integrity but will also question the correctness of Business Intelligence (BI). It is said that there has never been an executive who has not received two reports with the same data but different numbers. Utilizing the data is easy if the data is correct and of great quality. For data to benefit the organization, data governance ensures the management of data in the correct way using quality material. You can ethically monetize the data of your organization by utilizing the capabilities of Data Governance. Data Governance and Data Management The accounts of an organization are governed by certain principles and policies that help in auditing and helps in effectively managing the financial assets of a company. Similar to what these principles and policies achieve for financial assets Data governance does for Data, Information or content assets. Now, data management is the data supply chain for a company. Data Governance and Data Management go hand in hand and should not exist without each other. Data management is the actual process or business function to develop and execute the plans and policies that enhance the value of data and information. To relate these two, we have the concept of governance ‘V’. The left side of the V represents governance – providing rules and policies to ensure the correct management of data and content life cycle, and the right represent the ‘hands on’ data management. The V also helps understand the separation of duties and responsibilities for both DG and DM. The DG area develops the rules, policies and procedures and the Information managers adhere to or implement those rules. At the convergence of ‘V’ are the activities that maintain the data life cycle for the organization. Roles and Responsibilities in DG As mentioned earlier Data Governance requires distinct delegation of roles and responsibilities. This is a key factor for Data Governance to survive and flourish. This includes: Data Stewards – Manage and maintain the data assets, and data quality while implementing the data policies. Data Owners – Responsible for the governance and stewardship of specific data domains and sets Data Governance Council – Executive body that sets the data governance policies, processes, and strategies. Data Custodians- Execute and impose data security measures and access controls. Development and Deployment of DG Once data governance is considered in an organization, it means the problem arising with data due to lack of governance is being acknowledged. Data Governance is an essential element of comprehensive Enterprise Information management (EIM). When EIM solutions like Business Intelligence (BI) or Master Data Management (MDM) are implemented then DG is considered. MDM and DG are always implemented together for the expansion of EIM. The delivery framework for Data governance has five key areas of work. Each phase has a set of activities that help enhance the DG Program. Also, it is represented as a cycle below as it is usually iterative. For developing and deploying a data governance framework that is robust the following activities are involved: Engagement: Clear vision of the necessity and scope of the DG initiative. Aligning it with the organizations strategic priorities and engaging all stake holders to support DG Strategy: A set of requirements built to achieve organization goals and initiatives. Architecture & Design: Design and description of new enterprise capabilities and operating models that are embraced by stakeholders Implementation: Plan to deploy and invest in data governance tools and technology. Ensure that data governance is made operational. Operation & Changes: Operational and embedded set of BAU capabilities that enhance any activity using data. Monitor DG activities and measure the KPIs to assess effectiveness of the implemented framework Use Cases of DG There is wide usage of Data governance across industries. This includes: Regulatory compliance assurance: A data governance framework is implemented to comply with regulations such as GDPR, CCPA, and HIPAA. Data Quality Improvement: Data governance processes help improve the reliability, accuracy, and consistency of data. Strengthen decision-making: Leveraging data governance to provide stakeholders with access to high-quality, trusted data for informed decision- making. DG Vendors and Tools Numerous tools are available in the market to support Data Governance, listing a few: Collibra: Data governance workflows and processes can be operationalized to deliver great quality and trusted data across your enterprise Informatica CDGC: Using Cloud Data Governance and Catalog you can discover, understand, trust, and access your data to improve decision-making and govern analytics IBM InfoSphere Information Governance Catalog: A web-based tool that helps deliver trusted and meaningful information through a governed data catalog The first change an organization needs to bring for data monetization success is to get its organization data literate. Data management should be as much a part of an organization as budgets and risk. Data governance and management are both market-driven and to achieve maximum benefit you need to have these capabilities placed effectively.

In the bustling world of business, seamless data exchange is paramount. Enterprises rely on Electronic Data Interchange (EDI) to facilitate the exchange of business documents in a standardized format. EDI enables companies to exchange documents like purchase orders, invoices, and shipping notices with their trading partners efficiently and reliably. In this blog post, we'll delve into the significance of EDIs, introduce Talend as a powerful tool for EDI integration, and showcase how Pingahla's expertise has led to the development of an EDI accelerator, revolutionizing B2B integration processes. Electronic Data Interchange (EDI) is the electronic exchange of structured business data between different organizations. It replaces traditional paper-based methods with electronic formats, enabling seamless communication between trading partners. EDIs are widely used across various industries such as retail, manufacturing, healthcare, and logistics to automate transactions and streamline processes. By standardizing data formats and communication protocols, EDIs enhance operational efficiency, reduce errors, and accelerate business cycles. While specialized EDI tools exist in the market, many businesses leverage general-purpose integration platforms like Talend for their data integration needs. Talend stands out as a versatile tool due to its robust features, scalability, and ease of use. With Talend, organizations can integrate data from disparate sources, transform it according to business requirements, and load it into target systems seamlessly. Talend's graphical interface, extensive connectors, and built-in data quality features make it a preferred choice for complex integration projects, including EDI implementations. At Pingahla, we understand the challenges businesses face in integrating EDIs into their existing infrastructure. Leveraging our expertise in data architecture and pipeline development, we've created an innovative solution - the Pingahla EDI Accelerator. This accelerator is designed to streamline the integration of EDIs by harnessing the power of tools that organizations already love, such as Talend. With Pingahla's EDI Accelerator, businesses can ingest EDIs in real-time and efficiently process the data into specified databases or template table architectures. Our template tables are meticulously designed to accommodate various use cases for each EDI, ensuring flexibility and scalability. Whether you're dealing with purchase orders, invoices, or shipping notifications, our accelerator simplifies the integration process, allowing you to focus on core business activities. Now, let's take a closer look at the process flow involved in Pingahla's EDI Accelerator. Below is a diagram illustrating the workflow: EDI files are ingested in real time through Pingahla's EDI accelerator on Talend. Talend will detect the type of input EDI (EDI 850 in this example) and send it to a subjob that parses out all of the relevant information from the EDI 850 file into customer tables. The customer tables are example tables for the sake of showing our process. Once the data is in the tables, the customer ERP system is able to modify and use the EDI data or generate new data to be placed into the customer tables. These same tables feed into a second Talend job that generates an EDI 855. The file is sent elsewhere via FTP. The original EDI is conveniently achieved after the ingestion process is complete. By partnering with Pingahla, businesses can leverage our expertise to expedite their B2B integration efforts. Our EDI Accelerator empowers organizations to seamlessly exchange data with trading partners, enabling faster decision-making and improved operational efficiency. Say goodbye to tedious manual processes and embrace automated B2B integration with Pingahla. Ready to streamline your B2B integration processes? Get in touch with Pingahla today to learn more about our EDI Accelerator and how it can transform your business. With our proven track record and dedication to excellence, we're committed to helping you achieve success in the digital age. Unlock the full potential of your data with Pingahla. Connect with us today and embark on a journey towards seamless B2B integration!

In the world of data, the processes of Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are two of the most common methods for preparing data for analysis. Both processes have their advantages and disadvantages, and the method that best suits your needs will depend on the specific requirements your organization has. The terms ELT and ETL are fundamental but often confused. Although both refer to data manipulation and transfer processes, the differences in their approaches can have a significant impact on how organizations store, process, and use their data. In this blog, we will explain the key differences between ELT and ETL. What is ETL? ETL stands for Extract, Transform, Load. This process has long been the standard approach to data integration. It involves: Extracting data from various sources. Transforming these data, often in an intermediate storage area, by applying a series of rules or functions to convert them into the desired format for further analysis. Loading the transformed data into a target system, such as a data warehouse Advantages of ETL Data Control and Quality: By transforming the data before loading it, ETL allows for more thorough cleaning and quality control. This can be important to ensure that the data is transformed correctly and consistently. Performance: By performing the transformation before loading, ETL can reduce the load on the target system. The ETL process can provide better performance for large data loads. This is because transformation operations can be carried out in parallel with loading operations. Security: Minimizes security risks by processing the data before loading, which is crucial when handling sensitive data. Challenges of ETL Flexibility: It may be less adaptable to changes in data sources or data schemas. This is because the transformation operations must be performed before the data is loaded into the data warehouse or analysis system. Speed: The process can be slower, as the data must be transformed before being loaded. Higher Cost: The ETL process can be more expensive than the ELT process. This is because more hardware and software are required to perform the transformation operations. What is ELT? ELT, on the other hand, involves loading data directly into the target system and then transforming it within that system. This approach leverages the computational power of modern storage systems and is effective for large datasets, especially in cloud-based environments. Advantages of ELT Efficiency and Scalability: ELT is more efficient in handling large volumes of data, offering greater scalability and speed thanks to processing in modern storage systems, such as those based in the cloud. Flexibility: Offers greater adaptability to different types and formats of data, which is essential in environments where data changes rapidly or comes from diverse sources. Challenges of ELT Data Quality Management: This can present challenges in data quality, as the transformation occurs after loading. Technological Dependence: Requires advanced storage systems with high processing capacity. Key Differences ETL follows a more traditional approach. In this process, data is first extracted from its original sources. Then, before being loaded into the data warehouse, it is transformed in an intermediate system. This transformation can include cleaning, normalization, aggregation, and other operations necessary to ensure that the data is consistent and of high quality. This method is particularly valuable in environments where the quality and accuracy of data are critical, such as in the financial sector or in regulated environments where a high degree of data compliance and security is required. ELT, on the other hand, represents a paradigm shift driven by modern cloud storage technology. Here, data is extracted and loaded directly into the target system. The transformation occurs within this system, leveraging its robust processing capacity. This approach is ideal in scenarios where large volumes of data are handled, such as in big data and real-time analytics, as it allows for greater speed and flexibility in the processing and analysis of data. Which is Better? The best method for you will depend on your specific requirements. If you need more control over the transformations performed on the data, or if you need to perform complex or customized transformations, then the ETL process might be the best option for you. However, if you need to simplify the process, reduce costs, or improve speed for large data loads, then the ELT process might be a better choice. Practical Examples ETL in Healthcare Industry: For a hospital integrating patient data from multiple sources, ETL is essential to ensure the accuracy and privacy of data before it is stored in a centralized system. ETL in the Financial Industry: Used to integrate and transform financial data, ensuring accuracy and regulatory compliance. ELT in Social Media Analysis: A digital marketing company uses ELT to quickly process and analyze large volumes of social media user behavior data, enabling them to identify trends in real-time. Conclusion The choice between ELT and ETL should be based on factors such as data volume, specific processing requirements, and the available technological infrastructure. It is not simply a matter of preference, but depends on factors such as technological infrastructure, type and volume of data, and the specific needs of the business. Understanding these differences and selecting the right approach is crucial for maximizing the efficiency and effectiveness of data management in your organization. While ETL focuses on data quality and control before loading, ELT leverages the processing power of modern systems to accelerate the integration and transformation of large volumes of data.