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A modern data architecture must be integrated, modular, open, governed, AI-assisted, product-oriented, shared, and multi-modal. Do you agree? See my new BARC report, "Upgrade Your Data Architecture for AI Breakthroughs," on Informatica's website: https://lnkd.in/g9Qm7GBk It's a tall order! Tell us how you tackle data architecture. > Integrated A modern data architecture integrates old and new elements across all layers of the stack to provide AI models with the rich and diverse inputs they need. It also integrates data pipelines and applications to help automate financial transactions, customer service, and other business processes. > Modular To appreciate modularity, watch your kids make things with Legos. Data teams need similar flexibility to add, remove, or update modular elements in an iterative fashion. They might need to scale up a compute cluster when moving an ML model into production, replace a malfunctioning pipeline, or implement a new script to transform training data. > Open An open data architecture integrates easily with a broad ecosystem of popular commercial and open-source elements. Open APIs and formats enable data teams to maintain data mobility and tool interoperability across various applications, platforms, data centers, clouds, and regions. > Governed A modern architecture governs and secures data in several ways. Data platforms and analytical tools authenticate users, authorize their actions by role, and document activities for auditing purposes. In addition, master data management (MDM) and observability tools monitor and safeguard data quality. Functions such as these reduce the risk of untrustworthy outputs, mishandling of intellectual property, and compliance issues. > AI-assisted AI “copilots” make humans more productive by generating scripts, documenting artifacts, suggesting next steps, and so on. For example, data platforms and pipeline tools now feature copilots that help data teams store, integrate, master, catalog, and observe datasets. And analytical tools have copilots to help explore and visualize data. > Product-oriented Data products are standard, reusable datasets or artifacts that make data consumption more efficient and reliable. A modern data architecture supports the creation and delivery of data products that range from financial tables to refined training data, pipeline schemas, or predictive ML models. > Shared Teams must share data products across platforms, clouds, regions, and companies. Various types of data exchanges enable them to publish, describe, request, use, and re-use data products as part of either monetary or non-monetary arrangements. > Multi-modal Diverse use cases, AI models, and data types require a multi-modal architecture that supports all major data integration modes: ETL, ELT, change data capture, streaming, and data virtualization. #data #ai #dataarchitecture

Data Engineer Workflow A data engineer's workflow is essential for transforming raw data into actionable insights, enabling organizations to make data-driven decisions. Planning 🔸Identify goals and requirements for data pipelines or products. 🔹Chart a roadmap that aligns with business objectives. DataIngestion 🔸Collect data from diverse sources such as databases, APIs, and IoT devices. 🔹Handle both structured and unstructured data formats. DataTransformation 🔸Cleanse and normalize data to ensure consistency & accuracy. 🔹Apply transformations to convert data into a usable format. DataStorage 🔸Store processed data in appropriate systems, such as data warehouses or data lakes. 🔹Ensure that data is organized for efficient retrieval & analysis. Data Serving 🔸Deliver transformed data to end-users, including data scientists & analysts. 🔹Utilize BI tools and dashboards for data visualization. Stakeholder Understanding 🔸Recognize who will use the data and their specific requirements. 🔹Tailor the workflow to meet the needs of different user groups, such as executives and analysts. Technological Specifications 🔸Define the tools and technologies that will be utilized, such as ETL (Extract, Transform, Load) frameworks and data processing platforms. 🔹Consider automation opportunities to streamline repetitive tasks, such as data cleaning. Scalability & Performance 🔸Design workflows that can scale with increasing data volumes & velocities. 🔹Implement modular architectures that facilitate easy updates and maintenance. Data Governance & Security 🔹Ensure compliance with data privacy regulations and internal governance policies. 🔸Implement access controls and monitoring to safeguard sensitive data. Common Frameworks & Tools Data engineers often leverage various frameworks and tools to facilitate their workflows: 🛡️Apache Airflow: An open-source tool for orchestrating complex data workflows as directed acyclic graphs (DAGs), allowing for easy monitoring & management. 🛡️Apache NiFi: A user-friendly framework that provides a drag-and-drop interface for designing data flows, making it easy to automate data ingestion & processing. 🛡️AWS Step Functions: A cloud-based service that allows for the orchestration of workflows and state management, particularly useful for serverless applications 🛡️Data Pipeline Tools: Tools like Google Cloud Dataflow, Azure Data Factory and others that help automate data ingestion, transformation & loading processes Data Complexity: Managing multiple data sources and varied data types can complicate workflows Scalability: Ensuring that workflows can handle increasing data loads without performance degradation. Integration Issues:  Seamlessly integrating disparate systems and data sources. Real-Time Processing:  Handling data in real-time requires robust architectures and technologies. Performance Tuning:  Continuously optimizing workflows for speed and efficiency.

Comprehensive and insightful.

A Data is everything it's the only key that let any business to go up & down. A Complete Lifecycle of Data being Information.

Data Engineer Workflow A data engineer's workflow is essential for transforming raw data into actionable insights, enabling organizations to make data-driven decisions. #Planning 🔸Identify goals and requirements for data pipelines or products. 🔹Chart a roadmap that aligns with business objectives. #DataIngestion 🔸Collect data from diverse sources such as databases, APIs, and IoT devices. 🔹Handle both structured and unstructured data formats. #DataTransformation 🔸Cleanse and normalize data to ensure consistency and accuracy. 🔹Apply transformations to convert data into a usable format. #DataStorage 🔸Store processed data in appropriate systems, such as data warehouses or data lakes. 🔹Ensure that data is organized for efficient retrieval and analysis. #DataServing 🔸Deliver transformed data to end-users, including data scientists and analysts. 🔹Utilize BI tools and dashboards for data visualization. #Stakeholder Understanding 🔸Recognize who will use the data and their specific requirements. 🔹Tailor the workflow to meet the needs of different user groups, such as executives and analysts. #Technological Specifications 🔸Define the tools and technologies that will be utilized, such as ETL (Extract, Transform, Load) frameworks and data processing platforms. 🔹Consider automation opportunities to streamline repetitive tasks, such as data cleaning. #Scalability and Performance 🔸Design workflows that can scale with increasing data volumes and velocities. 🔹Implement modular architectures that facilitate easy updates and maintenance. #Data Governance and Security 🔹Ensure compliance with data privacy regulations and internal governance policies. 🔸Implement access controls and monitoring to safeguard sensitive data. #Common Frameworks and Tools Data engineers often leverage various frameworks and tools to facilitate their workflows: 🛡️Apache Airflow: An open-source tool for orchestrating complex data workflows as directed acyclic graphs (DAGs), allowing for easy monitoring and management. 🛡️Apache NiFi: A user-friendly framework that provides a drag-and-drop interface for designing data flows, making it easy to automate data ingestion and processing. 🛡️AWS Step Functions: A cloud-based service that allows for the orchestration of workflows and state management, particularly useful for serverless applications 🛡️Data Pipeline Tools: Tools like Google Cloud Dataflow, Azure Data Factory and others that help automate data ingestion, transformation and loading processes #Data Complexity: Managing multiple data sources and varied data types can complicate workflows #Scalability: Ensuring that workflows can handle increasing data loads without performance degradation. #Integration Issues: Seamlessly integrating disparate systems and data sources. #Real-Time Processing: Handling data in real-time requires robust architectures and technologies. #Performance Tuning: Continuously optimizing workflows for speed and efficiency. Gif Credit Respective owner

DE workflow is very crucial and everything needs to be inplace and functional Data Value - transforming raw data into actionable insights for decision making

Data Engineer Workflow A data engineer's workflow is essential for transforming raw data into actionable insights, enabling organizations to make data-driven decisions. #Planning 🔸Identify goals and requirements for data pipelines or products. 🔹Chart a roadmap that aligns with business objectives. #DataIngestion 🔸Collect data from diverse sources such as databases, APIs, and IoT devices. 🔹Handle both structured and unstructured data formats. #DataTransformation 🔸Cleanse and normalize data to ensure consistency & accuracy. 🔹Apply transformations to convert data into a usable format. #DataStorage 🔸Store processed data in appropriate systems, such as data warehouses or data lakes. 🔹Ensure that data is organized for efficient retrieval & analysis. #DataServing 🔸Deliver transformed data to end-users, including data scientists & analysts. 🔹Utilize BI tools and dashboards for data visualization. #Stakeholder Understanding 🔸Recognize who will use the data and their specific requirements. 🔹Tailor the workflow to meet the needs of different user groups, such as executives and analysts. #Technological Specifications 🔸Define the tools and technologies that will be utilized, such as ETL (Extract, Transform, Load) frameworks and data processing platforms. 🔹Consider automation opportunities to streamline repetitive tasks, such as data cleaning. #Scalability & Performance 🔸Design workflows that can scale with increasing data volumes & velocities. 🔹Implement modular architectures that facilitate easy updates and maintenance. #Data Governance & Security 🔹Ensure compliance with data privacy regulations and internal governance policies. 🔸Implement access controls and monitoring to safeguard sensitive data. #Common Frameworks & Tools Data engineers often leverage various frameworks and tools to facilitate their workflows: 🛡️Apache Airflow: An open-source tool for orchestrating complex data workflows as directed acyclic graphs (DAGs), allowing for easy monitoring & management. 🛡️Apache NiFi: A user-friendly framework that provides a drag-and-drop interface for designing data flows, making it easy to automate data ingestion & processing. 🛡️AWS Step Functions: A cloud-based service that allows for the orchestration of workflows and state management, particularly useful for serverless applications 🛡️Data Pipeline Tools: Tools like Google Cloud Dataflow, Azure Data Factory and others that help automate data ingestion, transformation & loading processes #Data Complexity: Managing multiple data sources and varied data types can complicate workflows #Scalability: Ensuring that workflows can handle increasing data loads without performance degradation. #Integration Issues: Seamlessly integrating disparate systems and data sources. #Real-TimeProcessing: Handling data in real-time requires robust architectures and technologies. #Performance Tuning: Continuously optimizing workflows for speed and efficiency. Post credit & Gif Credit Respective owner

Amplifi invests in innovation to ensure we stay ahead of the curve and provide modern solutions to our clients that keep pace with the rapidly evolving data landscape. We work closely with analysts, technology partners, and clients to understand the market and relevant trends, helping shape and define the direction of our service catalog: 🗺️ Data & Analytics Strategy Every organization needs a robust data strategy that directly links to their overall business goals. That data strategy shouldn’t just be a stack of paper; it needs to be a roadmap with clearly defined tactical deliverables. ▶ Data Strategy ▶ Data Capability Assessments ▶ DCAM® & CDMC® Authorized Partner 🌐 Modern Data Ecosystems Gartner described 2023 as the year that turned data on its head. With numerous new approaches to data, we design, architect, and build modern data ecosystems that meet the requirements of today’s data challenges. ▶ Data Mesh & Data Fabric ▶ Microsoft Fabric ▶ Data & Analytics Architecture ▶ Data Warehouse / Data Lakehouse 💾 Management & Governance The foundation of any successful data initiative is the management and governance of data. Our team of consultants and data specialists provide strategy, technology implementation, and support in core areas. ▶ MDM / PIM ▶ Data Quality & Observability ▶ Data Governance ▶ Data Catalog / Metadata Management ▶ Data Migration 🧩 Data Integration & Engineering Our team of Data Integration specialists can help you define the correct processes and technology to gain control of your data pipelines, connect disparate structured and unstructured sources, and empower the data consumers within your business to access the data they need quickly and reliably. ▶ Data Integration ▶ ETL ▶ Data Virtualization 🔬 Analytics & Data Science We craft analytics operating models that allow value to be delivered at scale. Whether it's turbocharging your existing analytics or implementing best-in-class technology, our team will ensure you’re able to make better decisions with your data. ▶ Modern BI Development ▶ Advanced Analytics ▶ Data Science 🤖 Artificial Intelligence (AI) We use AI and LLMs to accelerate, augment, and architect modern data solutions. Our three-pronged approach empowers our clients to deliver real value from AI and their data. ▶ GenAI & LLMs ▶ Data Modeling & Data Discovery ▶ Retrieval Augmented Generation (RAG) 🤝 Data Culture While technology is core to good data, often the best way to ensure successful adoption is through your people. Our expertise and experience in delivering impactful data solutions for clients across various sectors mean we can see the bigger picture and help you achieve your objectives. ▶ Change Management ▶ Project Management ▶ Learning & Development Want to find out more? You can check out our full offering here ▶ https://goamplifi.com #moderndata #dataconsulting #datastrategy

Data Engineer Workflow A data engineer's workflow is essential for transforming raw data into actionable insights, enabling organizations to make data-driven decisions. #Planning 🔸Identify goals and requirements for data pipelines or products. 🔹Chart a roadmap that aligns with business objectives. #DataIngestion 🔸Collect data from diverse sources such as databases, APIs, and IoT devices. 🔹Handle both structured and unstructured data formats. #DataTransformation 🔸Cleanse and normalize data to ensure consistency and accuracy. 🔹Apply transformations to convert data into a usable format. #DataStorage 🔸Store processed data in appropriate systems, such as data warehouses or data lakes. 🔹Ensure that data is organized for efficient retrieval and analysis. #DataServing 🔸Deliver transformed data to end-users, including data scientists and analysts. 🔹Utilize BI tools and dashboards for data visualization. #Stakeholder Understanding 🔸Recognize who will use the data and their specific requirements. 🔹Tailor the workflow to meet the needs of different user groups, such as executives and analysts. #Technological Specifications 🔸Define the tools and technologies that will be utilized, such as ETL (Extract, Transform, Load) frameworks and data processing platforms. 🔹Consider automation opportunities to streamline repetitive tasks, such as data cleaning. #Scalability and Performance 🔸Design workflows that can scale with increasing data volumes and velocities. 🔹Implement modular architectures that facilitate easy updates and maintenance. #Data Governance and Security 🔹Ensure compliance with data privacy regulations and internal governance policies. 🔸Implement access controls and monitoring to safeguard sensitive data. #Common Frameworks and Tools Data engineers often leverage various frameworks and tools to facilitate their workflows: 🛡️Apache Airflow: An open-source tool for orchestrating complex data workflows as directed acyclic graphs (DAGs), allowing for easy monitoring and management. 🛡️Apache NiFi: A user-friendly framework that provides a drag-and-drop interface for designing data flows, making it easy to automate data ingestion and processing. 🛡️AWS Step Functions: A cloud-based service that allows for the orchestration of workflows and state management, particularly useful for serverless applications. 🛡️Data Pipeline Tools: Tools like Google Cloud Dataflow, Azure Data Factory, and others that help automate data ingestion, transformation, and loading processes. #Data Complexity: Managing multiple data sources and varied data types can complicate workflows. #Scalability: Ensuring that workflows can handle increasing data loads without performance degradation. #Integration Issues: Seamlessly integrating disparate systems and data sources. #Real-Time Processing: Handling data in real-time requires robust architectures and technologies. #Performance Tuning: Continuously optimizing workflows for speed and efficiency.

A Guide to Medallion Architecture Ever feel overwhelmed by messy data? The medallion architecture can be your hero! It's a powerful data organization technique used in lakehouses to progressively improve data quality. Here's a breakdown: What is a Medallion Architecture? Imagine a medallion, a layered ornament. The medallion architecture reflects this with tiered data organization: 1. Bronze Layer (Raw Data): This is the foundation, holding the unprocessed data exactly as it arrives from various sources. Think of it as the raw ingredients waiting to be transformed. 2. Silver Layer (Cleansed Data): Data gets a makeover here! In this layer, the raw data is cleansed, de-duplicated, and transformed into a consistent format. It's like prepping your ingredients for a delicious meal. 3. Gold Layer (Business-Ready Data): The final course! Here, data is further refined and optimized for specific business needs. This could involve creating reports, dashboards, or feeding advanced analytics models. It's like plating your dish for a satisfying meal. As data progresses through these layers, its quality improves, ensuring reliable insights for data analysts and business users. Benefits of the Medallion Architecture: 1. Simplicity: The design is easy to understand and implement, even for complex data landscapes. 2. Incremental Updates: Only new or changed data is processed, saving valuable time and resources. Imagine updating just a single ingredient in your recipe instead of starting from scratch! 3. Recoverability: Made a mistake? No worries! You can recreate any layer from the raw data, providing a safety net. 4. Data Reliability: Medallion leverages ACID transactions (ensuring data consistency) and time travel features (allowing you to revert to past data states). Think of it as having a recipe you can trust and the ability to go back and adjust if needed. 5. Flexibility: Medallion architecture embraces the ELT (Extract, Load, Transform) approach, prioritizing fast data loading and applying complex transformations later. This allows for greater agility and adaptation to evolving business needs. Imagine having the freedom to experiment with your recipe without starting over. Beyond the Basics: The medallion architecture can also integrate with existing data marts and warehouses, enabling a unified platform for advanced analytics and machine learning. This is like combining your favorite cuisines in one amazing culinary experience! In conclusion, the medallion architecture empowers you to transform raw data into valuable insights. Its simplicity, reliability, and flexibility make it a compelling choice for businesses seeking to unlock the true potential of their data. #datamanagement #lakehouse #datadesign #dataquality #dataengineering #datatransformation #ELT #ETL #businessintelligence #datadrivendecisions #lakehousearchitecture #databricks #data Image source: Databricks