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.

En el mundo de datos, los procesos de extracción, transformación y carga (ETL) y extracción, carga y transformación (ELT) son dos de los métodos más comunes para preparar datos para el análisis. Ambos procesos tienen sus propias ventajas y desventajas, y el método que mejor se adapte a sus necesidades dependerá de los requisitos específicos que la organización requiera. Los términos ELT y ETL son fundamentales, pero a menudo se confunden. Aunque ambos se refieren a procesos de manipulación y transferencia de datos, las diferencias en sus enfoques pueden tener un impacto significativo en cómo las organizaciones almacenan, procesan y utilizan sus datos. En este blog, explicaremos las diferencias clave entre ELT y ETL. ¿Qué es ETL? ETL son las siglas de Extract, Transform, Load (Extraer, Transformar, Cargar). Este proceso ha sido durante mucho tiempo el enfoque estándar para la integración de datos. Se trata de: Extraer (extract) datos de diversas fuentes. Transformar (transform) estos datos, a menudo en un área de almacenamiento intermedio, aplicando una serie de reglas o funciones para convertirlos al formato deseado para su posterior análisis. Cargar (load) los datos transformados en un sistema de destino, como un data warehouse. Ventajas del proceso ETL Control y calidad de datos: Al transformar los datos antes de cargarlos, el ETL permite una limpieza y un control de calidad más exhaustivos. Esto puede ser importante para garantizar que los datos se transformen de manera correcta y consistente. Rendimiento: Al realizar la transformación antes de la carga, el ETL puede reducir la carga en el sistema de destino. El proceso ETL puede proporcionar un mejor rendimiento para cargas masivas de datos. Esto se debe a que las operaciones de transformación se pueden realizar en paralelo con las operaciones de carga. Seguridad: Minimiza los riesgos de seguridad al procesar los datos antes de cargarlos, lo que es crucial cuando se manejan datos sensibles. Desafíos del proceso ETL Flexibilidad: Puede ser menos adaptable a los cambios en las fuentes de datos o en los esquemas de datos. Esto se debe a que las operaciones de transformación se deben realizar antes de que los datos se carguen en el almacén de datos o el sistema de análisis. Velocidad: El proceso puede ser más lento, ya que los datos deben ser transformados antes de ser cargados. Mayor costo: El proceso ETL puede ser más costoso que el proceso ELT. Esto se debe a que se requiere más hardware y software para realizar las operaciones de transformación. ¿Qué es ELT? ELT, por otro lado, implica cargar datos directamente en el sistema de destino y luego transformarlos dentro de este sistema. Este enfoque aprovecha la potencia computacional de los sistemas de almacenamiento modernos y es eficaz para grandes conjuntos de datos, especialmente en entornos basados en la nube. Ventajas del proceso ELT Eficiencia y Escalabilidad: ELT es más eficiente en el manejo de grandes volúmenes de datos, ofreciendo una mayor escalabilidad y velocidad gracias al procesamiento en sistemas modernos de almacenamiento, como los basados en la nube. Flexibilidad: Ofrece una mayor adaptabilidad a diferentes tipos y formatos de datos, lo que es esencial en entornos donde los datos cambian rápidamente o provienen de diversas fuentes. Desafíos del proceso ELT Gestión de la Calidad de Datos: Puede presentar desafíos en la calidad de los datos, ya que la transformación ocurre después de la carga. Dependencia Tecnológica: Requiere sistemas de almacenamiento avanzados con alta capacidad de procesamiento. Diferencias Clave ETL sigue un enfoque más tradicional. En este proceso, los datos se extraen primero de sus fuentes originales. Luego, antes de ser cargados en el almacén de datos, se transforman en un sistema intermedio. Esta transformación puede incluir limpieza, normalización, agregación, y otras operaciones necesarias para garantizar que los datos sean coherentes y de alta calidad. Este método es particularmente valioso en entornos donde la calidad y la precisión de los datos son críticas, como en el sector financiero o en entornos regulados donde se requiere un alto grado de conformidad y seguridad de datos. ELT, por otro lado, representa un cambio en el paradigma impulsado por la tecnología moderna de almacenamiento en la nube. Aquí, los datos se extraen y se cargan directamente en el sistema de destino. La transformación ocurre dentro de este sistema, aprovechando su capacidad de procesamiento robusta. Este enfoque es ideal en escenarios donde se manejan grandes volúmenes de datos, como en big data y análisis en tiempo real, ya que permite una mayor velocidad y flexibilidad en el procesamiento y análisis de los datos. ¿Cuál es el mejor? El mejor método para usted dependerá de sus requisitos específicos. Si necesita un mayor control sobre las transformaciones que se realizan en los datos, o si necesita realizar transformaciones complejas o personalizadas, entonces el proceso ETL puede ser la mejor opción para usted. Sin embargo, si necesita simplificar el proceso, reducir el costo o mejorar la velocidad para cargas masivas de datos, entonces el proceso ELT puede ser una mejor opción. Ejemplos Prácticos ETL en la Industria de la Salud: Para un hospital que integra datos de pacientes de múltiples fuentes, ETL es esencial para garantizar la precisión y la privacidad de los datos antes de que se almacenen en un sistema centralizado. ETL en la Industria Financiera: Utilizado para integrar y transformar datos financieros, asegurando precisión y cumplimiento normativo. ELT en Análisis de Redes Sociales: Una empresa de marketing digital utiliza ELT para procesar y analizar rápidamente grandes volúmenes de datos de comportamiento de usuarios en redes sociales, lo que le permite identificar tendencias en tiempo real. Conclusión La elección entre ELT y ETL debe basarse en factores como el volumen de datos, los requisitos específicos de procesamiento y la infraestructura tecnológica disponible., no es simplemente una cuestión de preferencia, sino que depende de factores como la infraestructura tecnológica, el tipo y volumen de datos, y las necesidades específicas del negocio. Comprender estas diferencias y seleccionar el enfoque adecuado es crucial para maximizar la eficiencia y efectividad de la gestión de datos en su organización. Mientras que ETL se centra en la calidad y el control de los datos antes de la carga, ELT aprovecha la potencia de procesamiento de los sistemas modernos para acelerar la integración y transformación de grandes volúmenes de datos.

It is an application used for data processing. Cloud secure agents allow secure communication through the firewall between the Informatica Cloud and the Organization. HOW TO DOWNLOAD AND INSTALL CLOUD SECURE AGENT FOR WINDOWS Here are the steps to download the Cloud Secure Agent. 1. When you log in to the Informatica Cloud, you will see a window as the image below; select Administrator. 2. Once you select the Administrator option, from the left side menu choose Runtime Environments -> Download Secure Agent. 3. After clicking the Download Secure Agent button, you must select which type of operating system you will work with. In this case, we will install it on a Windows machine. Click on “Copy Install token” and paste it into a Notepad or any text editor; it will be used later. 4. Open the folder where the .exe file to install Secure Agent was saved. Right click and select Run as Administrator. 5. Click on Next. 6. Click Install 7. Once the Secure Agent has been installed, it will open a new window requesting the username and the installation token. Enter the username you used to access Informatica Cloud. Paste the installation token code and click on Register. 8. After clicking on Register, the Secure Agent will display a new window with the status; uploading all services takes a few minutes. 9. If you want to review if all services are running from Informatica Cloud, click on Administrator -> Runtime Environments -> Secure Agent Name (Machine) 10. In the Windows server, you can check if all services for Informatica Cloud are running. 11.If Administrator permissions and privileges are required, right-click on the Informatica Cloud in the Windows services and enter the username and password. 12.       Click Apply, and Ok. Restart the Secure Agent to apply the changes.

Role of AI in Data Quality Data quality is a crucial factor for any data-driven project, especially involving Machine Learning (ML) and Artificial Intelligence (AI). Data quality is referred to as the degree to which the data meets expectations. Poor data quality affects the performance, accuracy, and reliability of AI systems which can lead to inaccurate, unreliable & biased results of AI systems affecting the trustworthiness & value of AI systems. Traditional data quality practices are manual, time-consuming, and error-prone. They cannot handle increasing volume, variety, and velocity of data. Testing data quality is also a complex process. It involves aspects such as data validation, data cleaning, data profiling, etc. which require a lot of human effort and expertise. Therefore, testing data quality is a key challenge for data professionals. This is where AI can help us in testing data quality. Using AI and ML algorithms, it can automate and optimize various aspects of data quality assessment making the testing process smarter, faster, and more efficient. Problems that can be solved Some of the common problems that can be solved using AI-based testing for data quality are: Data validation Data validation is the process of checking whether the data conforms to the predefined rules, standards, and formats such as checking whether the data types, formats, ranges, and values are correct and consistent. AI-based testing can automate data validation by using ML models to learn the rules and patterns from the data and apply them to new or updated data. For example, an AI-based testing tool can automatically detect and flag missing values, duplicates, or invalid values in the data. Data profiling Data profiling is the process of analyzing the structure, content, and quality of the data. Data profiling helps us to understand the characteristics and behavior of the data, as well as identify potential issues or opportunities for improvement. For example, calculating the statistics, distributions, correlations, and dependencies of the data attributes. AI-based testing can automate data profiling by using ML models to extract and summarize relevant information from the data. For example, an AI-based testing tool can automatically generate descriptive statistics, visualizations, or reports on the data quality metrics. Data cleansing Data cleansing is the process of improving the quality of the data by removing or correcting errors, inconsistencies, anomalies, or duplicates in the data. Data cleansing helps us to enhance the accuracy, consistency, reliability, and completeness of the data. AI-based testing can automate data cleansing by using ML models to learn from existing or external data sources and apply appropriate transformations or corrections to the data. For example, an AI-based testing tool can automatically replace missing values based on predefined rules or learned patterns. Data Enrichment Data enrichment is the process of adding value to the data by augmenting or supplementing it with additional or relevant information from other sources. Data enrichment can help increase the richness, relevance, and usefulness of the data. For example, adding geolocation information based on postal codes or product recommendations based on purchase history. AI-based testing can automate data enrichment by using ML models to learn from existing or external data sources to generate or retrieve additional information for the data. For example, an AI-based testing tool can automatically add geolocation information based on postal codes by using a geocoding API or recommend products based on purchase history by using a collaborative filtering algorithm. Advantages of AI-based testing Some advantages of using AI for testing are: Automation AI can help in automating various tasks or processes related to data quality assessment or improvement. AI can help in validating, cleansing, profiling, or enriching the data by using ML models to learn from existing or external data sources and by applying appropriate actions or transformations. Optimization AI can help in optimizing various parameters or aspects related to data quality. AI can help in finding the optimal rules, formats, standards, or constraints by using ML models to learn from the existing or external data sources and apply the most suitable solutions for the data. This can improve the effectiveness, accuracy, and efficiency and enhance the quality of data. Insight AI can help in providing insights and feedback for data quality improvement. AI can help in generating descriptive statistics and visualizations to profile the structure, content, and quality of the data and provide insights on correlations, missing values, duplicates, etc. It can also help in identifying potential issues or scope for improvement in the data quality by providing recommendations for resolving or enhancing them. Drawbacks or Limitations of using AI Despite having its advantages, there are also some drawbacks or limitations that need to be considered. Some of the drawbacks are: Complexity Using AI requires a lot of technical knowledge to design, implement, and maintain the AI and ML models used for testing the data. It also requires a lot of computational resources and infrastructure to run and store the models and the data. Moreover, it may involve various issues such as privacy, security, accountability, and transparency for using AI and ML for testing. It can be a complex and challenging process that requires careful planning, execution, and management. Uncertainty The recommendations, assumptions, or predictions made by the AI and ML models may not always be accurate, reliable, or consistent in their outcomes. They may also not always be able to capture the dynamic or evolving nature of the data or the project requirements. Therefore, using AI for testing can bring some uncertainty or risk in the testing process that needs to be monitored and controlled. Dependency The quality, availability, and accessibility of the existing or external data sources used by the AI and ML models for learning plays a crucial role in testing. However, these data sources may not always be relevant, fair, or representative of the data or the project objectives. Moreover, they may not always be compatible or interoperable with the formats or standards used by the AI and ML models or the tools or platforms used for testing the data. Future of AI Testing Using AI for testing is a promising technique to overcome the challenges and limitations of traditional testing methods. It can automate and optimize various aspects of data quality by using AI and ML algorithms and applying appropriate actions or transformations to the data. It can also provide insights and feedback for data quality improvement by using descriptive statistics and visualizations. When it comes to testing the quality of data using AI, there are different methods and tools available. These include platforms that use AI to offer complete solutions and specific tools that use AI to address specific issues. Depending on the goals and requirements of the project, users can select the most appropriate approach or tool for their testing needs. The use of AI in testing presents a host of challenges and limitations that require careful implementation, evaluation, and maintenance of the AI and ML models. To ensure optimal performance, accuracy, reliability, and fairness, it is crucial to continually monitor and update these models. It should be noted, however, that AI cannot fully replace human judgment and intervention in guaranteeing data quality. Rather, it serves as a valuable tool to augment human efforts through automated assistance and guidance. AI-powered testing for data quality is a rapidly growing field with great potential for innovation. As technology continues to progress, so will the methods and tools for improving data quality through AI. The future of using AI for testing data quality is promising and full of possibilities.

Power BI (Microsoft) and Tableau (Salesforce) are both popular business intelligence (BI) tools used for data visualization and analysis. Every year, they are both positioned as leaders in the market by Gartner because of their significant adoption and widespread use across various industries. However, they have some differences in terms of features, functionality, and target user base. Here are some key distinctions between Power BI and Tableau: Ease of use: Power BI is generally considered to be more user-friendly, especially for beginners. It has a simpler interface, it’s easier to navigate and since it’s a Microsoft product, it integrates with many popular tools that are used in most companies, like Teams, Excel and PowerPoint. Tableau, on the other hand, has a steeper learning curve and can be more complex to use. Data connectors: Power BI has a wider range of data connectors and can connect to more data sources than Tableau. For example, it’s a lot easier to connect Power BI to Microsoft Dynamics 365 Business Central, a popular ERP software. On the other hand, since Tableau is part of the Salesforce group, it can access Salesforce data and reports more efficiently because it is not limited by the amount of data it can import from Salesforce. Power BI: Tableau: Pricing: Power BI has a lower entry-level price point, with a free version and a more affordable Pro version at 10$ per month per user or developer. Tableau, on the other hand, is more expensive and has a higher entry-level price point. The monthly Tableau subscription for developers is 70$ per month per developer and the license per viewer is 15$ per month per viewer. Power BI: Tableau: Integration: Both Power BI and Tableau offer integration capabilities with various data sources and other platforms. Power BI can be integrated and embedded in a wide range of applications, including Web Apps. It can also fully integrate with Microsoft Suite, such as Teams, PowerPoint, Excel and soon, Outlook. Tableau also allows users to embed dashboards on the Web and connect to a wide range of data sources, including databases, cloud storage platforms, spreadsheets, and web applications. Tableau and Salesforce seamlessly integrate with each other since Tableau is part of the Salesforce group. That enables productive data analytics that brings benefits to the users of the popular CRM. Tableau also offers many marketing and social media connectors that Power BI don’t, like Facebook Ads Power BI: Tableau: Customization: Tableau is generally considered to be more customizable and flexible than Power BI, because it has more visuals and advanced features for data analysis and visualization. However, Power BI allows you to download custom visuals created from the community of developers on the Power BI visuals marketplace. Power BI visuals marketplace: Power BI core visuals: Tableau: Collaboration: Both tools offer collaboration features, but now that Power BI has released its developer mode, it is considered to be more robust in terms of co-development, source control, Continuous Integration and Continuous Delivery (CI/CD). Ultimately, the choice between Power BI and Tableau depends on your specific needs, preferences and the other tools and software your company is already using. Power BI may be a better choice for businesses with limited budgets and less complex data analysis needs. Tableau may be a better choice for organizations with more complex data needs and a larger budget.