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As the importance of data takes more center stage, we have more and more businesses that claim to be data-driven. As companies increase their sources of data, their data storage, pipelines, and usage tend to grow at an enormous speed. With the growth of data, the chances of inaccuracy, errors, and data downtime grow as well. As we are much aware that the decision-making of a company springs from data and the unreliability of data is a pain point for every industry today. It is difficult to make decisions based on capricious data and hence eliminating instances of downtime, bad data, missing data, and the like is going to reach new heights by prioritizing data observability. What is Data Observability? For data engineers, the next crucial step to effectively manage any incident detection within their data pipelines is to establish data observability. In their organizations, data engineers devote half of their time to maintaining these pipelines due to frequent disruptions and breakdowns, which hinder them from effectively constructing data-driven products. This is where Data Observability comes into the picture. Data observability refers to an organization's comprehensive awareness of the well-being and condition of the data present in its systems. Ultimately, it all boils down to the ability to closely track and oversee a pipeline of data that is observed by someone. Let’s walk through the problems that data engineers face: Process quality Data quality or Data integrity Data lineage Process Quality First concern is if the data is moving, or the pipeline is operational. Speed in data processing could be core to the business. Data Integrity Once the functionality of the pipeline has been confirmed, the next step is to examine the activities occurring at the level of the data set. Imagine if data becomes vulnerable, misplaced, or corrupted. As an example, there may be a modification in the schema where we anticipate having 10 columns, but the new schema only has 9 columns. This could pose an issue as the data will have consequences for a downstream process that relies on the data set. Alternatively, if there are any modifications to the data, it will ultimately cause corruption of the subsequent data. Data Lineage This is about how things are connected to dependent pipelines and data sets downstream. The essence of data observability is captured in this statement! To put it simply, Data observability refers to the process of taking action to identify incidents in the original data source, data warehouse, or downstream at the product level. This allows the data engineers team to be promptly notified whenever there is a problem. The team would have the capability to rectify and proactively address the issue, thereby ensuring that it does not affect customers further down the line and, ultimately, avoid significant and expensive consequences for the business. The principles of data observability involve promptly identifying anomalies at their origin, resolving them quickly, understanding their exact location, and predicting their impact on subsequent individuals or processes. To proactively identify, resolve, and prevent irregularities in data, data observability tools utilize automated monitoring, root cause analysis, data lineage, and data health insights. Using this method leads to improved data pipelines, heightened team efficiency, strengthened data management strategies, and ultimately, increased customer contentment. Salient Features of Data Observability The purpose is to understand the essential changes in both organizational and technological perspectives to establish a data observability system that enables flexible data operations. To safeguard the practicality of data observability, it is vital to merge the following actions into its configuration. Monitoring A dashboard that allows a pragmatic viewpoint of your pipeline or system is referred to as monitoring. Alerting Notifications about predictable incidences and anomalies. Alerting permits you to detect complex conditions defined by a rule within the Logs, Infrastructure, Uptime, and APM apps. When a condition is met, the rule tracks it as an alert and responds by triggering one or more actions. Tracking Competence to establish and monitor specific occurrences. Comparison Observations made at different intervals will be compared and any abnormal alterations will be identified through alerts. Analysis Involuntary issue detection that regulates your pipeline and data state, referred to as analysis. Logging maintaining track of an occurrence using a standardized method to enable more rapid resolution. SLA tracking The characteristic of SLA Tracking involves measuring the cohesion of data quality and pipeline metadata to established standards. Data Observability - a future must-have The ability of data teams to be agile and make improvements to their products largely depends on their data observability. If there is no such system, a team's infrastructure or tools cannot be dependable as the identification of errors would take too long. If you do not invest in this important component of the DataOps framework, you will have reduced flexibility in creating new features and enhancements for your customers, resulting in a waste of money. Once Data observability is in place data teams will prevent time consumption in debugging and error fixing and there will be more businesses that will strive to be data driven.

In today's digital landscape, data has become the driving force behind successful marketing strategies. Digital marketers rely on a wealth of information about their ad campaigns, customers, and more to effectively target new and existing customers. Additionally, the need for advanced solutions and technology has become paramount to capitalize on this data-driven approach. In this blog post, we will delve into why digital marketers require more data and better solutions to achieve superior customer targeting and optimize their campaigns. Understanding Customers: In order to effectively target customers, digital marketers need comprehensive data to understand their target audience better. By gathering and analyzing data on customer demographics, behaviors, interests, and preferences, marketers can gain valuable insights into what motivates their customers. This enables them to tailor their campaigns with precision, delivering relevant and personalized messages that resonate with the audience. The more data available, the more refined and accurate the targeting becomes, leading to higher conversion rates and customer engagement. Refining Customer Segmentation: Data empowers digital marketers to segment their customer base into distinct groups based on various characteristics. With more data points at their disposal, marketers can create more granular and refined segments, resulting in more effective targeting. By identifying different customer personas and understanding their unique needs, preferences, and pain points, marketers can develop highly targeted campaigns that cater to each segment's specific requirements. The result is an increased likelihood of attracting and retaining customers within each segment. Optimizing Ad Campaigns: Accurate data is invaluable in optimizing digital advertising campaigns. By monitoring and analyzing campaign performance metrics such as click-through rates (CTRs), conversion rates, bounce rates, and return on ad spend (ROAS), marketers can gain insights into what works and what doesn't. Armed with this information, they can make data-driven decisions to refine their ad creatives, targeting parameters, and campaign strategies. This iterative process ensures that the marketing efforts are continuously optimized for maximum effectiveness and efficiency. Personalized Customer Experiences: Today's customers crave personalized experiences, and data plays a crucial role in delivering them. By collecting data on customer preferences, purchase history, browsing behavior, and interactions with the brand, digital marketers can create tailored experiences across various touchpoints. From personalized product recommendations to customized email campaigns and dynamic website content, data-driven personalization enhances customer satisfaction, engagement, and loyalty. Enhanced Customer Retention: Data-driven marketing goes beyond acquiring new customers; it also focuses on retaining existing ones. By analyzing customer data, marketers can identify patterns and signals that indicate potential churn or dissatisfaction. With this knowledge, they can implement targeted retention strategies such as personalized offers, loyalty programs, and proactive customer support. By leveraging data to proactively address customer needs and concerns, marketers can increase customer loyalty and lifetime value. Better Solutions and Technology: To harness the power of data effectively, digital marketers require robust solutions and technology. Advanced analytics tools, customer relationship management (CRM) systems, data management platforms (DMPs), and artificial intelligence (AI) technologies enable marketers to collect, analyze, and leverage data at scale. These solutions provide actionable insights, automate processes, and facilitate personalized interactions, empowering marketers to make data-driven decisions with agility and precision. Data has become an indispensable asset for digital marketers seeking to target new customers and optimize their campaigns. By collecting and analyzing data about their customers and ad campaigns, marketers gain valuable insights that inform their strategies and drive superior results. Additionally, access to better solutions and technology allows marketers to harness the full potential of data, delivering personalized experiences and enhancing customer retention. Embracing data-driven approaches and investing in advanced solutions is key to unlocking the true power of digital marketing in today's fast-paced and competitive. In addition, partnering with companies such as Pingahla provide the additional support system a digital marketer now needs to be successful.

In today's digital landscape, cloud computing has emerged as a game-changer, empowering organizations to scale their operations, improve efficiency, and drive innovation. However, choosing a single cloud platform can be limiting, as each cloud vendor brings its own strengths and weaknesses. This is where a multi-cloud strategy enters the picture, enabling organizations to leverage the best of multiple cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). In this blog post, we'll explore the importance of a multi-cloud strategy and its key benefits for organizations. Ensuring High Availability and Failover: One of the primary advantages of a multi-cloud strategy is the ability to achieve high availability and failover capabilities. Organizations can mitigate the risk of service disruptions and downtime by distributing workloads across multiple cloud platforms. Should one cloud provider experience an outage or performance issues, the workload seamlessly transitions to another provider, ensuring uninterrupted service delivery to end-users. AWS, Azure, and GCP all offer robust infrastructure and redundancy features, making them ideal for building a resilient multi-cloud architecture. Optimizing Data Load Latency: Geographical proximity plays a crucial role in determining data load latency. With a multi-cloud approach, organizations can strategically distribute their data across cloud providers' global data centers, placing it closer to end-users or specific regions. This reduces latency and enhances user experience, particularly for latency-sensitive applications. AWS, Azure, and GCP have extensive global footprints, allowing organizations to select data center locations that align with their target audience or compliance requirements. Mitigating Vendor Lock-In Risks: By adopting a multi-cloud strategy, organizations can reduce their dependence on a single cloud vendor, mitigating the risks associated with vendor lock-in. This freedom empowers organizations to negotiate better pricing, leverage competitive advantages, and retain technological flexibility. For instance, AWS offers a wide range of services and strong integration with other Amazon offerings, Azure brings seamless integration with Microsoft technologies, and GCP offers cutting-edge machine-learning capabilities. By leveraging each vendor's strengths, organizations can design a best-of-breed architecture tailored to their specific needs. Enhancing Disaster Recovery Capabilities: Disaster recovery is a critical aspect of business continuity planning. A multi-cloud strategy allows organizations to design robust disaster recovery architectures by utilizing different cloud providers. In the event of a disaster, data, and applications can be replicated and stored across multiple cloud platforms, ensuring rapid recovery and minimal data loss. AWS, Azure, and GCP offer comprehensive disaster recovery services, including backup, replication, and failover mechanisms, making them ideal multi-cloud disaster recovery strategy components. Leveraging Specialized Services and Innovation: Each cloud provider brings unique services, capabilities, and innovation to the table. By adopting a multi-cloud strategy, organizations can tap into a wide array of specialized offerings from AWS, Azure, and GCP. For example, AWS excels in scalable computing and storage services, Azure offers seamless integration with Microsoft's extensive ecosystem, and GCP stands out with its data analytics and machine learning tools. Leveraging these strengths empowers organizations to drive innovation, meet specific business requirements, and gain a competitive edge in their respective industries. In a rapidly evolving digital landscape, a multi-cloud strategy has become essential for organizations seeking to optimize their cloud investments, enhance resilience, and unlock the full potential of cloud computing. By harnessing the strengths of AWS, Azure, and GCP, organizations can ensure high availability, reduce latency, mitigate vendor lock-in risks, enhance disaster recovery capabilities, and leverage specialized services for innovation.

In today's data-driven world, businesses rely heavily on robust data management solutions to extract actionable insights and drive strategic decision-making. Legacy systems, such as Informatica's PowerCenter, Metadata Manager, and others, have served organizations well over the years. However, the time has come to embrace the next generation of data management platforms, such as Informatica's Intelligent Data Management Cloud (IDMC). This blog post explores why current legacy Informatica customers should consider the transition and highlights the benefits of adopting IDMC. Comprehensive Solution Suite: Informatica's IDMC combines a comprehensive suite of data management products under one unified platform. With IDMC, customers gain access to a range of powerful tools, including PowerExchange (PWX), Data Quality (DQ), Data Transformation (B2B DT), Big Data Management (BDM), Data Integration HUB (DIH), and B2B Data Exchange (DX). This integration eliminates the need to purchase these products separately, streamlining the overall data management ecosystem. Enhanced Scalability and Flexibility: As organizations grow and their data management needs evolve, scalability becomes paramount. IDMC offers the scalability required to handle the ever-increasing volumes of data generated by modern businesses. Moreover, its cloud-based architecture allows for elastic scaling, enabling organizations to scale up or down based on demand. This flexibility ensures that businesses can adapt to changing requirements efficiently and cost-effectively. Next-Level Data Quality and Governance: Maintaining data quality and ensuring regulatory compliance is critical for any organization. IDMC leverages Informatica's advanced data quality and governance capabilities, empowering businesses to cleanse, standardize, and enrich their data easily. The platform's comprehensive data governance features, built-in metadata management, and business glossary capabilities enable organizations to establish a solid foundation for accurate and trusted data. Streamlined Operations and Simplified Management: One of the primary advantages of IDMC is its unified and user-friendly interface, allowing for simplified data management and operations. Organizations can leverage a centralized platform to design, develop, deploy, and monitor their data integration workflows and transformations. IDMC's streamlined management and monitoring capabilities lead to increased productivity and operational efficiency. Harnessing the Power of Big Data: In the era of big data, organizations must effectively handle vast amounts of diverse data sources. IDMC's Big Data Management (BDM) capabilities empower businesses to leverage the full potential of big data by seamlessly integrating and processing data from various sources. With IDMC, organizations can unlock insights from structured, semi-structured, and unstructured data, enabling them to make data-driven decisions confidently. Conclusion: In today's rapidly evolving data landscape, legacy data management systems can become a bottleneck for organizations striving to harness the power of their data. By migrating to Informatica's Intelligent Data Management Cloud (IDMC), current legacy Informatica customers can unlock many benefits. With its comprehensive suite of data management products, enhanced scalability, robust data quality and governance features, simplified management interface, and the ability to harness the power of big data, IDMC is poised to drive organizations toward a future of optimized data management and improved business outcomes. So, suppose you are still relying on legacy Informatica solutions. In that case, it's time to consider making the leap to IDMC and embark on a transformative journey toward intelligent and comprehensive data management such as IDMC.

Implementing Informatica B2B Gateway for EDI requires several steps and specialized skills and expertise with the technology. Here is a general high-level overview of the process: Define the requirements: Determine the business requirements for your EDI implementation. This includes identifying the types of documents you will exchange, the trading partners you will work with, and the specific data elements that must be included in each document. Configure the B2B Gateway: Install and configure the Informatica B2B Gateway software according to the specifications of your environment. This includes configuring the communication protocols, security settings, and other system parameters. Create and map EDI documents: This is where the majority of heavy-lifting in development is done. Use the B2B Gateway tools to create the EDI documents you will be exchanging with your trading partners. You will need to build mappings to map the data elements from your internal systems to the EDI document format and vice versa. Test the EDI transactions: Work with your trading partners to test the EDI transactions. This includes sending and receiving test documents, validating the data, and resolving any issues that arise. Deploy the system: Once testing is complete, you can deploy the B2B Gateway system for production use. Monitor and maintain the system: Monitor the system to ensure that it is operating correctly and resolve any issues that arise. You may need to update the configuration or make changes to the mapping as your business requirements change.

Electronic Data Interchange, or EDI, is the transfer of business documents electronically between two trading partners in a standardized format. This process eliminates the need for paper-based documents and manual entry of data, reducing errors, and improving efficiency. EDI has become more widespread with the growth of the internet and the need for companies to streamline their supply chains. EDI can be used for a variety of business documents, such as purchase orders, invoices, and shipping notices. The benefits of using EDI include faster processing times, improved accuracy, and reduced costs. By eliminating paper-based documents and manual data entry, companies can process transactions more quickly and with fewer errors. This can lead to faster turnaround times, improved customer service, and increased productivity. In addition to these benefits, EDI can also help companies improve their relationships with trading partners. By using a standardized format, companies can ensure that their trading partners receive accurate and complete information, which can help to build trust and reduce the risk of disputes. EDI is not without its challenges, however. Setting up an EDI system can be complex and time-consuming, and there can be issues with compatibility between different systems. In addition, there may be resistance to change from employees who are used to working with paper-based documents. Despite these challenges, many companies have found that the benefits of using EDI outweigh the costs. If you’re interested in learning more about EDI, please reach out as we extensive experience implementing EDI solutions in various industries such as healthcare, manufacturing, retail, and more using partnered technology products such as Informatica.

Talend CDC (Change Data Capture) es una herramienta que realiza un proceso de integración de datos, que replica y además sincroniza datos desde una fuente a diferentes destinos en tiempo real. Muchas veces se requiere que mantengamos nuestras bases de datos sincronizadas y son diferentes los casos de uso que pueden ser solucionados con CDC como por ejemplo: Tener nuestro DWH alineado con los últimos datos de nuestras fuentes transaccionales. Nuestra arquitectura híbrida necesita mantener el ambiente onpremise alineado con entorno cloud. Tenemos un sistema de replicación que consume mucho rendimiento, en el que necesitamos realizar consultas complejas sobre algunas columnas específicas de nuestros datos para saber si es un nuevo registro, si ha sufrido cambios o fue eliminado. Estamos usando nuestra base de datos transaccional como fuente de reportes, y por ende afectamos su rendimiento. Le gustaría tener un backup online. Quiere una base de datos distribuida. Entre otros. CDC puede ser usado en diferentes ambientes: onpremise, híbrido o cloud, y cuenta con conectores a los diferentes proveedores cloud como son Snowflake, AWS, o Azure, y servicios gestionados como Kafka, AWS kinesis o Azure Eventhub; adicionalmente cuenta con conectores y soporte onpremise como IBM(AS400), que es usado frecuentemente por entidades financieras y lo hace único en el mercado. ¿Cómo funciona? CDC identifica los diferentes cambios que hayan en la fuente y los replica en los destinos, estos datos replicados provienen de las diferentes operaciones realizadas como: INSERT DELETE UPDATE Las fuentes de datos que están produciendo cambios, tienen la capacidad de almacenar logs con los eventos de transacciones que se han realizado sobre los datos, estos logs pueden ser en diferentes formatos, pues varían de acuerdo al fabricante. El agente CDC actúa monitoreando y recolectando esos eventos transaccionales una vez, para almacenarlos en sus propios archivos de logs (Journal), esto, en caso de que sea necesario reusarlos en diferentes Jobs de replicación o de alguna otra manera, sin embargo, este proceso es ajeno a los recursos del sistema y no interactúa con ellos. En los casos en los que ya tengamos una base de datos creada y poblada como fuente, necesitaremos hacer solo una replicación completa, ya que de ese punto hacia adelante Talend CDC capturará los cambios realizados. Es posible aplicar scripts a los datos en la fuente para seleccionarlos, transformarlos o realizar agregaciones como también aplicar cambios en el destino previo a la integración. Su configuración es realizada en una consola gráfica en la que seleccionamos nuestra fuente y destino, esta opera con diferentes roles como: Administrador Operador Espectador Las réplicas son creadas en un ambiente y allí se llamarán modelos, un modelo contiene las fuentes y destinos; una vez configurados, podemos agregar opciones adicionales para estos, como por ejemplo, programar las ejecuciones, cambiar el framework con el que hacemos la integración, o supervisar el job a través de logs dentro de la herramienta, o fuera de ella en archivos de log. Existen diferentes frameworks con los que CDC puede hacer replicaciones de datos, cada uno de ellos cuenta con capacidades adicionales como agregar columnas en el destino, brindar transformaciones, agregar identificadores de secuencias, o incluso para trabajar con destinos de big data. Demo de una réplica Estándar Configuraciones Iniciales En este blog enseñaré una réplica estándar, por lo que seleccionaremos como framework “Free”, lo que significa que no se agregarán columnas o datos adicionales a los que ya se encuentran en la fuente, estaré usando la versión de Talend Change Data Capture V 7.15.0 Para este ejemplo ya tengo creadas las bases de datos fuente y destino en SQL Server a la cuales nombré “training_source” y “training_target”, y en la fuente tengo una tabla llamada “students” mientras que en el destino no he creado nada aún. Así mismo, la configuración de Talend CDC hacia la fuente y los diferentes componentes necesarios para el correcto funcionamiento de la herramienta ya se encuentran instalados, estos son: Motor de Captura Motor de Fuente Motor de destino Gestor de Talend CDC Conector ODBC 32 bits para SQL Server en el DSN (Data Source Administrator) SQL Server Comencemos: Iniciamos configurando la fuente en el menú File -> Source subscribe y diligenciaremos los datos de acuerdo con nuestra configuración de la fuente: Name (Alias): El nombre que quiera IP address: Es la ubicación del motor de origen de CDC donde se definió la conexión de la instancia de base de datos, esta configuración se realizó durante la instalación y configuración de CDC Source type: El motor de la BD fuente Instance or Server name: El que esté configurado en tu servidor Luego notaremos como en la fuente nos mostrará “TrainingSource” con un botón verde que nos servirá para conectarnos a esta: Una vez nos conectamos y veremos cómo se muestran los diferentes ambientes configurados donde podremos crear los modelos, para este ejemplo usaremos Training como nuestro ambiente: Ahora configuramos el journal, por lo que vamos al menú Source -> Journal Management Presionar clic derecho sobre la DB creada training_source y elegimos la opción “Start DB logging process” Luego podremos seleccionar un directorio en el que queramos almacenar el journal y presionamos Ok. Aún en el Journal Management hay que seleccionar la tabla que queremos monitorear y clic derecho sobre esta y presionar la opción “Start table logging process for”: Nota: Debe existir una llave primaria configurada en la tabla para iniciar el proceso de monitoreo sobre esta Después de iniciar el proceso de monitoreo sobre la tabla “students” necesitamos verificar que el journal está funcionando, para esto vamos a hacer un leve cambio sobre alguno de los registros de esta tabla: Nota: Hemos cambiado la ciudad John Doe de New York a Miami Ahora nuevamente desde el Journal Management clic derecho sobre “training_source” y “Display Journal”: Veremos el único receiver disponible por el cambio que acabamos de realizar: Lo seleccionamos y vamos a la pestaña “Posts” y desde allí veremos los cambios que se realizaron: Nota: Se observa el cambio de ciudad, el primer registro es “New York” y el segundo es “Miami” lo que nos garantiza el correcto funcionamiento del journal. Crear Réplica: El primer paso es crear un espacio de configuración dentro de un ambiente, cada réplica dentro de un mismo ambiente comparte los mismos parámetros que las demás y pueden ser administradas juntas. En la pestaña “Environment” clic sobre “Add” y diligenciamos los campos como el siguiente ejemplo: Environment: Código único del ambiente que se creará Source code: Código único para la fuente, debe ser de 3 caracteres Environment type: Para este ejemplo es “Training” Description: Se recomienda agregar descripción Clic sobre “Add” y tendremos el ambiente creado: Nota: El ambiente contiene una una pestaña para fuente donde se crea el modelo y otra pestaña del destino, adicionalmente tres pestañas donde se mostrarán propiedades de la replicación, y elementos gráficos de la réplica para la visualización del proceso. Me aseguro de estar en la pestaña “Source” y presiono clic derecho sobre “Models” y luego en “Add”: Y configurar el modelo de la siguiente manera: Nota: Se observan dos pestañas adicionales “Table Options” y “Script” a las cuales dejaremos por default. Model: Un código único para el modelo Description: Descripción del modelo Type: Para esta replicación usamos JOURNAL, sin embargo existe EXTRACT que permite una extracción full Clic en “Add” y el modelo estará disponible dentro del folder “Models” en la pestaña “Source” del “Environment”: El siguiente paso es agregar Tablas al modelo, expandiendo el Modelo en el botón [+], luego clic derecho sobre “Tables” y finalmente clic en “Add” Una ventana aparecerá para seleccionar las tablas que agregaremos al modelo: Nota: Como se puede observar, estamos usando el framework “Free” que no genera cambios adicionales a nuestros datos Con el botón “Query” podremos listar todas las tablas disponibles Seleccionar las tablas, en este caso “students” Mover la tabla seleccionada al modelo Finalizar La tabla aparecerá creada dentro de nuestro modelo: El siguiente paso es crear el destino, desde la pestaña “Target” de nuestro ambiente damos clic derecho en “Targets” y luego “Add”: Se abrirá la ventana para el nuevo Target: Target: Código único para el destino Description: Descripción del destino Target type: valor por default Name: Nombre de la instancia de nuestra base de datos destino Address: dirección del servidor donde está la base de datos destino Exclude: se llena automáticamente en cuanto ponemos el nombre de la instancia Entonces clic en “Add” y veremos nuestro target creado: El siguiente paso es crear una distribución que significa asociar nuestro modelo a un destino, lo podemos realizar desde la pestaña “Source” o “Target”; expandir nuestro target creado y clic sobre “Distribute”: Y movemos el Modelo desde el panel izquierdo al derecho y clic sobre “Ok”: Una ventana se abrirá automáticamente para finalizar la configuración de la distribución: Seleccionar el tipo de base de datos Doble clic sobre el conector Se abrirá una ventana para ingresar los datos de nuestra base de datos de destino Clic en “Ok” para agregarla Si la conexión es correcta, una ventana de notificación nos mostrará “Connection Successful” y nos mostrará las bases de datos disponibles allí, y finalmente clic en “Add”: Podemos ver ahora desde la pestaña “Map” cómo el target T01 está asociado al modelo M01: El siguiente paso es crear la tabla destino, entonces desde la pestaña “Target” damos clic en el modelo “M01” luego seleccionar la tabla a replicar, clic derecho sobre esta y finalmente “Create Target Table”: Luego de esto veremos que una nueva ventana aparece, mostrando el DDL de la nueva tabla usando la instancia de la base de datos de destino: Clic en “Execute” y luego chequeamos en la base de datos de destino para evidenciar que la tabla fue creada allí, pero se encuentra vacía: Ejecutar Réplica: Ahora que ya está creado el target, el modelo y la distribución, debemos ejecutar la réplica; desde la pestaña “Map” clic derecho sobre el Modelo M01 y luego clic en “Properties”: Desde la pestaña “Recovery” seleccionamos el check box “Load”: Luego vamos a la pestaña “Activity”, una ventana de confirmación nos pedirá si deseamos recargar todas las tablas en esa distribución, a lo que diremos “Yes”. Ahora desde la pestaña “Activity” clic sobre el botón “Start”: Después de iniciar la replicación, los campos de la sección “Counters” y “Last operation” nos mostrarán los resultados y así mismo la distribución quedará activa y a la espera de algún cambio que ocurra en la fuente para replicarlo automáticamente a menos de que deseemos detenerla: Nota: Como se observa, tenía 3 registros en la fuente y esos mismos fueron seleccionados, enviados y agregados. Cuando el modelo esté activo, será de color verde, de lo contrario será amarillo, o puede ser rojo si se encuentran errores durante la ejecución: Ahora se debe revisar en la tabla destino para evidenciar que los registros fueron replicados: Finalmente detenemos la replicación dando clic derecho sobre el modelo que aparece en verde y clic en “Stop” y de esa manera finalizamos la réplica estándar. En resumen: Talend CDC nos permite diferentes modos y ofrece muchas más características para la replicación en tiempo real; en el ejemplo anterior realizamos una replicación simple con los mismos campos en fuente y destino dentro del mismo servidor, así como una ejecución manual dado que pueden ser también programadas. En los próximos posts, enseñaré otras características o funciones con las que Talend CDC trabaja, además de evidenciar las diferencias con otros frameworks allí disponibles. Fredy Antonio Espitia Castillo Talend Developer Certified https://www.linkedin.com/in/fredy199601/

Talend CDC (Change Data Capture) is a tool that performs a data integration process that replicates and syncs data from one source to different targets in real-time. In a lot of business scenarios, it is required to keep databases synchronized, and there are many of those use cases that can be solved with Talend CDC, such as: Keeping the DWH aligned with the latest data from transactional sources Hybrid architecture needs to keep the cloud environment aligned with the on-premises data environment A very performance-consuming replication system that needs to perform complex queries on specific columns of our data to find out if it is a new, changed, or deleted record. A transactional database is used as a reporting source, thus affecting its performance. Having an online backup Having a distributed database CDC can be used in different environments: on-premises, hybrid, or cloud, and it has connectors to different cloud providers such as Snowflake, AWS, or Azure, and managed services such as Kafka, AWS kinesis or Azure Eventhub. It also has connectors and support for on-premises servers such as IBM AS400, which is frequently used by financial entities and makes it unique in the market. How does Talend CDC work? CDC identifies the different changes that are in the source and replicates them in the target source. The replicated data comes from the different operations carried out such as: INSERT DELETE UPDATE The data sources that are producing changes can store logs with the transaction events that have been carried out on the data. These logs can be in different formats, varying according to the manufacturer. The CDC agent works by monitoring and collecting those transactional events once, to store them in its own log files (Journal), in case it’s necessary to reuse them in different replication. However, this process is oblivious to system resources and does not interact with them. In cases where we already have a database created and populated as a source, we will need to only do one full replication. From that point forward, Talend CDC will capture the changes made. It’s possible to apply scripts to the data in the source to select, transform or perform aggregations as well as apply changes to the target prior to integration. Its configuration is done in a graphical console in which we select our source and target. It operates with different roles as: Administrator Operator Viewer The replicas are created in an environment, and there will be called models; a model contains the sources and targets; Once configured, we can add additional options for these, such as scheduling the executions, changing the framework with which we integrate, or supervising the job through logs in the tool or log files. There are different frameworks with which CDC can do data replications, and each of them has additional capabilities, such as adding columns in the target, providing transformations, adding sequence identifiers, or even working with big data targets. Demo of a Standard Replication Initial Settings In this blog, I will show a standard replica, so we will select the framework as “Free”, which means that no additional columns or data will be added to what is already in the source; I will be using Talend Change Data Capture version V 7.15 .0. For this example, I have already created the source and target databases in SQL Server, which I named "training_source" and "training_target" respectively; in the source, I have a table called "students" while in the target, I have not created anything yet. Likewise, the configuration of Talend CDC towards the source and the different components necessary for the correct functioning of the tool are already installed, these are: Capture Engine Source Engine Target Engine Talend CDC Manager 32-bit ODBC connector for SQL Server in the DSN (Data Source Administrator) SQL Server Let's start: We start by configuring the source in the File -> Source subscribe menu and fill in the data according to our source configuration: Name (Alias): The name that you prefer IP address: Is the location of CDC source engine where the DB instance connection has been defined, this setup was done during the CDC installation and configuration Source type: The engine of the source DB Instance or Server name: The one configured on your server Then we notice that in the source it will show us "Training Source" with a green button that will help us to connect to it: When we connect to that source, we will see that the different configured environments where we can create the models are shown, for this example, we will use Training as our environment: Now we configure the journal, so we go to Source -> Journal Management Right click on the created DB "training_source" and choose the option “Start DB logging process” Then we can select the directory in which we want to store the journal and press Ok. Still in Journal Management, we have to select the table we want to monitor and right click on it and then, press the option “Start table logging process for”: Note: A primary key must be configured in the table to start the monitoring process on this. After starting the monitoring process on the "students" table, we need to verify that the journal is working, for this we are going to make a slight change on one of the records on this table: Note: We have changed John Doe's city to Miami instead of New York Now again in the Journal Management right click on “training_source” and “Display Journal”: We will see the only receiver available, which is the one written for the change we just made: We select it and go to the "Posts" tab and from there we will see the changes that were made: Note: The change of city is shown, the first record is "New York" and the second is "Miami" which guarantees the correct functioning of the journal. Creating the Replication: The first step is to create a configuration space within an environment; each replica within the same environment shares the same parameters and can be managed together. In the "Environment" tab, click on "Add" and fill in the fields like the following example: Environment: unique code of the environment to be created Source code: Unique code for the source, must be 3 characters exactly Environment type: For this example it’s “Training” Description: It’s recommended to add a description Click on “Add” and we will have the environment created: Note: The environment contains a tab for the source where the model is created and another tab for the target. Additionally, three tabs where the replication properties will be displayed, and graphic elements of the replica to visualize the process. Now from the “Source” tab right click on “Models” and then on “Add”: And configure the model as follows: Note: Two additional tabs “Table Options” and “Script” are shown, for which we will use the configuration set by default. Model: A unique code for the model Description: Description of the model Type: For this replication, we use JOURNAL, however, there is EXTRACT that allows a full extraction Click on “Add” and the model will be available in the “Models” folder in the “Source” tab of the “Environment”: The next step is to add Tables to the model, expanding the Model on the [+] button, then right-clicking on “Tables” and finally clicking on “Add” A window will appear to select the tables that we will add to the model: Note: As you can see, we are using the "Free" framework that doesn’t generate additional changes to our data With the "Query" button we can list all the available tables Select the tables, in this case "students" Move the selected table to the model Finish The table will appear created inside our model: The next step is to create the target, from the "Target" tab of our environment we right click on "Targets" and then "Add": The window for the new Target will open: Target: unique code for the target Description: Description of the target Target type: default value Name: Name of the instance of our target database Address: address of the server where the target database is located Exclude: it’s filled automatically as soon as we put the name of the instance Then click on “Add”, and we will see our created target: The next step is to create a distribution which means associating our model to a target, we can do it from the "Source" or "Target" tab; expand our created target or source, and click on “Distribute”: And we move the Model from the left panel to the right and click on “Ok”: A window opens automatically to finish the configuration of the distribution: Select the type of database Double-click on the connector A window will open to enter the data from our target database Click on “Ok” to add it If the connection is correct, a notification window will show us "Connection Successful" and will show us the databases available there, and finally click on "Add": We can now see from the "Map" tab that the target T01 is associated with the model M01: The next step is to create the target table, then from the "Target" tab we click on the "M01" model, then select the table to replicate, right-click on it, and finally "Create Target Table": After this we will see that a new window appears showing the DDL of the new table using the target database: Click on “Execute” and then we check in the target database to show that the table was created there, but it is empty: Run Replication: Now that the target, the model and the distribution are created, we must execute the replication; from the “Map” tab, right click on Model M01 and then click on “Properties”: From the "Recovery" tab we select the "Load" check box Then we go to the "Activity" tab, a confirmation window will ask us if we want to reload all the tables in that distribution, to which we will say "Yes". Now from the "Activity" tab click on the "Start" button After starting the replication, the fields of the "Counters" and "Last operation" section will show us the results and likewise, the distribution will remain active and waiting for any change that occurs in the source to replicate it automatically: Note: As shown, we had 3 records in the source and those same ones were selected, sent and added to the target. When the model is active, it will be green, otherwise it will be yellow, or it can be red if errors are encountered during execution: Now it must be checked in the target table to show that the records were replicated: Finally, we stop the replication by right-clicking on the model that appears in green and clicking on “Stop”. Talend CDC offers many more features for real-time replication. In the previous example, we perform a simple replication with the same source and target fields, as well as a manual execution since they can also be scheduled. In the next posts, I will show other features or functions that Talend CDC works with, as well as highlighting the differences with other frameworks available there. FAQ What is Talend CDC used for? Keeping a data warehouse aligned with live transactional sources, syncing on-premises and cloud environments in hybrid architectures, offloading reporting queries from a live transactional database, and maintaining an online or distributed backup — all without full re-extracts of the source data. Is Talend CDC real-time? Yes. It reads the source database's transaction logs (or triggers, depending on mode) continuously, so changes reach the target within seconds of the source commit rather than on a batch schedule. Which databases does Talend CDC support? Oracle, MySQL, DB2, PostgreSQL, Sybase, MS SQL Server, Informix, Ingres, Teradata, and IBM AS/400. Does Talend CDC affect source database performance? Minimally. Because it reads from transaction logs rather than querying the live tables, it avoids the load a polling-based comparison approach would create. How is Talend CDC different from Qlik Replicate or Debezium? All three do log-based change capture, but they differ in ecosystem fit: Talend CDC integrates natively with Talend/Qlik pipelines, Qlik Replicate is Qlik's broader enterprise replication platform with a wider target-system list, and Debezium is open-source and Kafka-native, better suited to teams already standardized on a Kafka streaming stack. The right choice depends on what's already in the stack, not which tool has more features on paper. Fredy Antonio Espitia Castillo Talend Developer Certified https://www.linkedin.com/in/fredy199601/

Last week on March 9, 2023, I attended the NYC Snowflake Data for Breakfast event. The event was packed with attendees from various industries and featured a lineup of great speakers who shared their experiences and insights on using Snowflake's platform. Also, to make things better, Snowflake had some great food and coffee flowing, which was needed since the event started at 8 am EST. Now the event kicked off with Brent Bateman, a Principal Sales Engineer at Snowflake, who discussed the capabilities of Snowflake, with a focus on its self-managed automated administration and unified governance platform. He highlighted how Snowflake is helping companies easily manage and access their data with less complexity and more agility. A lot of us know this today, but it was a good way to get the room to wake up and excited for the morning event. One thing Brent did not discuss though was the recent acquisition with Streamlit, but we all know there will be more to come on this in the future with Snowflake. Next, Jonathan Hyman, Co-Founder & CTO at Braze, shared how they use Snowflake to gain better insights into their customers and build more personalized experiences. He emphasized the importance of data-driven decision-making in today's business landscape and how Snowflake is an essential tool for achieving that goal. In addition, it was a breath of fresh air as Jonathan also walked us through at a high-level his current architecture and their customer journey. The event also featured a panel discussion with Jai Subrahmanyam, Senior Vice President, Head of Data Governance at The Blackstone Group, and Phil Andriyevsky, Partner- Wealth and Asset Management at EY. They shared how Snowflake has helped The Blackstone Group in leveraging data for better decision-making and driving business outcomes. They also discussed the value Snowflake has brought to their respective organizations in terms of scalability, performance, and cost savings. The event concluded with all speakers coming together to answer questions from the audience. Attendees had the opportunity to learn from Snowflake experts and industry leaders and gained valuable insights on how Snowflake can help their organizations effectively manage and utilize their data. Overall, this was a great event and extremely informative and insightful. Also, I look forward to exploring Snowflake capabilities and how they are being used with their current customers. Also, thanks again to the Snowflake Partner team for inviting Pingahla Colombia and Pingahla NorAm team to all the events being hosted around the world! Next up Snowflake Summit on June 26th! I can't wait to be in attendance.

As this week draws the Data for Breakfast events with our Snowflake partner. I thought it would be only right to put together a blog post on the latest news about Snowflake's Streamlit acquisition. When I saw this acquisition, I thought this was a great job by Snowflake! But I’m still waiting on Snowflake to purchase dbt...😊 Now some of you may not have heard about Streamlit. Now for those who have only heard about Steamlit now, let me share with you a quick blurb about the company that you can easily find on the web. Streamlit is an open-source framework that allows data scientists and machine learning engineers to build web applications for their data projects. With Streamlit, users can create interactive and customizable web interfaces for their machine-learning models, data visualizations, and data exploration tools without needing to know how to write front-end web code. Now, what is the value of Streamlit to organizations and companies like yours? Well, their software allows the ability to simplify and streamline the development process. From what I have read and seen on YouTube. Using Streamlit, companies can build and deploy interactive applications faster and with fewer resources than with traditional dev methods. Now the kicker for the solution's value is that it can lead to faster time-to-market for new products, and solutions, improved collaboration between data engineers, data scientists, and business users, and greater agility in responding to changing market needs. To me, this is a game-changer! Now if it can do all of this why would Snowflake would want to purchase Streamlit? Now I do not sit on the Snowflake board, work for the company, etc. I have very little insight on the WHY, but being a technologist, I would believe the reason Snowflake purchased Streamlit is due to many of the benefits I highlighted above which include; Allowing Snowflake customers to quickly build and deploy web applications on top of their data. This could help Snowflake differentiate its offering from competitors and provide additional value to its customers. Snowflake's expertise in data management and analytics could help streamline Streamlit's development process and provide access to a larger customer base. This could help accelerate Streamlit's growth and adoption in the market. The acquisition could potentially lead to synergies in research and development, with both companies sharing knowledge and expertise in the areas of data management, analytics, and web application development Either way, this is a win, win for Snowflake customers and partners like Pingahla. I am excited to be attending the upcoming NYC Snowflake Data for Breakfast and I hope this is a topic that will be discussed. Snowflake + Streamlit News: https://investors.snowflake.com/news/news-details/2022/Snowflake-Announces-Intent-to-Acquire-Streamlit-to-Empower-Developers-and-Data-Scientists-to-Mobilize-the-Worlds-Data/default.aspx

In today's data-driven world, data has become one of the most valuable assets for businesses. However, the increasing amount of data being generated and processed also poses significant risks to data security, privacy and data regulations. Therefore, it has become essential for businesses to implement robust data governance and data security practices to mitigate these risks. Now I understand, not all organizations have a clear data governance strategy or org in place, but I am seeing more and more of my customers make this investment due to data regulations such as CCPA, GDPR, and Virginia CDPA to name a few. However, business and development teams often face significant challenges while working with data governance and data security teams. These teams can slow down the development process, making it difficult for business and development teams to deliver new features and functionality on time. This delay can cause frustration for the business users, developers and may impact the overall success of the project. Despite these challenges, it's crucial for the business and development teams to work closely with data governance and data security teams. These teams are responsible for implementing the policies, procedures, and technologies that mitigate security risks, ensure data compliance, and protect sensitive information. Here are some reasons why: Mitigating security risks: Data governance and data security teams are responsible for identifying and mitigating security risks. These teams establish security policies and procedures, perform security assessments, and implement security controls to protect data from unauthorized access, theft, or misuse. By working closely with these teams, developers can ensure that the applications they develop are designed and implemented with security in mind, reducing the risk of security breaches. Ensuring data compliance: Data governance and data security teams are also responsible for ensuring that the organization's data practices comply with relevant laws and regulations, such as CCPA, Virginia CDPA, GDPR or HIPAA. These regulations have strict requirements for data protection, data privacy, and data access, which can be challenging for development teams to navigate. By working closely with data governance and data security teams, developers can ensure that their applications comply with these regulations, reducing the risk of non-compliance and potential legal and financial consequences. Protecting sensitive information: Data governance and data security teams are responsible for protecting sensitive information, such as personal information, financial information, and intellectual property. By working closely with these teams, developers can ensure that the applications they develop are designed and implemented with data protection in mind, reducing the risk of sensitive information being exposed or misused. Now, let's take a look at some examples of companies that have faced consequences for poor data governance or data security practices: Facebook: In 2018, Facebook was embroiled in a massive data privacy scandal involving the data analytics firm Cambridge Analytica. It was discovered that Cambridge Analytica had obtained data from millions of Facebook users without their consent, which was used to influence political campaigns. This scandal led to a massive loss of trust in Facebook, and the company faced intense scrutiny from regulators and lawmakers around the world. Equifax: In 2017, credit reporting agency Equifax suffered a massive data breach that exposed the personal information of approximately 143 million people. The breach was caused by a vulnerability in Equifax's web application framework, which had not been patched despite a security alert being issued months earlier. The company faced intense criticism for its poor data security practices, and it ultimately had to pay a $700 million settlement to consumers, regulators, and lawmakers. Citibank: In 2020, Citibank was fined over $400M due to risk management and data governance issues. The bank had failed to implement proper risk management practices and internal controls, which led to a massive error in its payments system. The error resulted in Citibank accidentally transferring nearly $900 million to a group of creditors of Revlon, the cosmetic company. Citibank was unable to retrieve the funds, and it ultimately had to absorb the loss. The incident highlighted the importance of robust risk management and data governance practices, as failure to implement these practices can result in significant financial losses and regulatory consequences. These examples illustrates how poor data governance and risk management practices can have severe consequences for organizations like these three examples. It's essential for business and development teams to work closely with data governance and security teams to ensure that the applications and solutions they develop are designed and implemented with these practices in mind. By doing so, organizations can reduce the risk of costly errors, data breaches, and regulatory fines. In conclusion, while working with data governance and data security teams can be challenging, it's essential for development teams to prioritize these practices. By doing so, business and developers can ensure that the applications they develop are designed and implemented with security and compliance in mind, reducing the risk of security breaches, non-compliance, and potential legal and financial consequences. The mind set that these two parts of the organization are different should not exist. Think of these two parts of the org as an extended part of your team wanting you on the business or delivery team to succeed while making sure the proper governance and security is in place!

At Pingahla we work with customers who are at different stages of their cloud journey. A common theme that we see is the selection of the right cloud data warehousing and analytics platform. This becomes especially important as the wrong choice may seriously hinder the speed and agility of digital transformation and the company's ability to respond to changing market dynamics. In this post we will evaluate and compare two major players in this space - Snowflake and DataBricks DataBricks and Snowflake are both cloud-based data warehousing and analytics platforms. While they share some similarities, there are also some key differences between the two. One major difference is that DataBricks is a unified analytics platform that combines data engineering, data science, and business analytics in a single environment, while Snowflake is a cloud-based data warehousing solution that focuses specifically on data storage and querying. Another difference is that DataBricks uses an in-memory computing model, which allows for fast processing of large amounts of data, while Snowflake uses a columnar storage model and separates compute and storage resources, which can make it more scalable and cost-effective for certain use cases. Additionally, DataBricks offers a range of integrated tools and services for data engineering, machine learning, and business intelligence, while Snowflake integrates with a variety of third-party tools and services for data integration, visualization, and analytics. Overall, the choice between DataBricks and Snowflake will depend on the specific needs and goals of an organization. DataBricks may be a better fit for organizations that need a full-featured analytics platform with integrated tools and services, while Snowflake may be a better fit for organizations that have specific data warehousing requirements and want a scalable and cost-effective solution. At Pingahla, we help solve complex data challenges for our customers. We are experts in building cloud based data warehousing and analytics solutions. To know more, please reach out at info@pingahla.com