Sam Lodaria’s Post

Why did the data catalog breakup with it's partner? because it couldn't handle so many relationships... on a serious note though, how many entities can your catalog responsively support? how many do you need to support, how does your catalog scale? Alex Solutions supports tens of millions, quite frankly, if the infrastructure is big enough to support the knowledge graph in all its glory, the limits are probably relatively boundless. but it remains an important question. Consider that a table is just one entity, if it has ten attributes it just jumped to 11 entities and those 10 attributes all have a relationship with the table. Your database now contains at least 21 records. Start adding views, stored procedures, ETL and reporting applications, it very quickly exploded to hundreds and then thousands and then millions of entries. Start adding data people, controls, technology describers and business processes, quality measures, KPIs and metrics and it extends further. Can your catalog adequately serve up the answers you need in this context?

What is a data warehouse. Really? Is it just the storage point of the different data sources across an enterprise or a sum of all the moving parts that make it possible? For the end user, who only sees data-at-rest, the lineage of data that were Extracted, Transformed and Loaded (ETL) to form the data is inconsequential; until there is a breakdown of the continuous integration/development (CI/CD) processes. That raises the question of points of failure. Several of the resources we depend on today are often reliant on a few points of failure, some of which are highlighted in red in the diagram below. Such that when they collapse for whatever reason, the scramble to fix begins. A few ways to handle these failures can be 1. the simply "after a few seconds, try again, pretty please automation; preferably with the same parameters" 2. Don't fail at all, by ensuring to optimize resource calls below throttling/unhealthy queuing thresholds 3. Windowing batch calls to the smallest possible size with latency in mind 4. Scaling resources to exceed requests 5. A few others While there are some points of failures even as far as the libraries used in open source software which are completely out of our control. We can ensure a 99.99% uptime for up-to-date data for business and technical users with just a few of these little techniques to ensure reliability. How are you minimizing CI/CD failures?

What is Data Integration? Data integration refers to the process of combining data from different sources and formats into a single, unified view. This can involve various techniques such as data warehousing, Extract, Transform, Load (ETL) processes, or more modern approaches like data virtualization and application programming interfaces (APIs). The goal of data integration is to provide users with a comprehensive and consistent view of data across disparate systems, enabling better decision-making, analysis, and insights. It helps organizations to break down data silos, improve data quality, and enhance overall efficiency in utilizing data assets. https://lnkd.in/g27AbmnN

🌐 Navigating the Data Migration Journey: Ensuring Seamless Transitions In today's digital landscape, businesses rely heavily on data to drive decisions and operations. As a seasoned professional in data migration, I understand the critical importance of ensuring a smooth transition when moving data from legacy systems to modern platforms. Here’s a glimpse into my approach: 1. Assessment and Planning: Before any migration begins, a thorough assessment of existing data structures and business requirements is essential. This phase sets the foundation for a well-planned migration strategy. 2. Strategy Development: Crafting a robust strategy involves defining migration goals, selecting appropriate tools like SQL Server Integration Services (SSIS), and outlining timelines and milestones. Clear communication with stakeholders is key to aligning expectations. 3. Execution with Precision: The migration process itself demands meticulous attention to detail. From data cleansing and transformation to rigorous testing, every step is geared towards preserving data integrity and minimizing downtime. 4. Post-Migration Validation: Once data is migrated, rigorous testing and validation ensure that everything functions as expected. This phase also includes training stakeholders on new systems to maximize adoption and utilization. 5. Continuous Improvement: Data migration is not just a one-time task but an ongoing journey. Regular evaluations and refinements based on user feedback and evolving business needs ensure that the data ecosystem remains agile and efficient. Data migration isn’t just about moving data; it’s about enabling businesses to leverage their data assets effectively. I’m passionate about driving successful migrations that empower organizations to thrive in a data-driven world. Let’s connect and discuss how we can navigate your data migration challenges together! #DataMigration #ITStrategy #BusinessTransformation #SSIS #DataIntegrity #DigitalTransformation

Discover essential best practices for data integration. Learn how to optimize data connectivity and management for seamless integration.

One of the biggest concerns in core system migration is crystal clear: “Will all our data be accurate in the new system?” Our client had every reason to worry about data quality: 1. New Processes: They were built directly on the contemporary application. 2. Complex Queries: With a vast volume of data points, they needed accurate and consistently structured queries. 3. Data Transformation: Many data points in the new database came from combining and transforming multiple sources. But here’s the good news! 💪 With a relentless focus on data quality and a commitment to continuous improvement, we successfully tackled these challenges head-on. Curious about how we did it? Check out the use case for insights and strategies that can help you in your own data migration journey! 👉 Let’s connect if you have questions! #DataMigration #SystemMigration #DataQuality

I want to share with you some insights on how to implement continuous integration and continuous delivery (CI/CD) for data pipelines. CI/CD is a software development approach where all developers work together on a shared repository of code – and as changes are made, there are automated build processes for detecting code issues. The outcome is a faster development life cycle and a lower error rate. CI/CD for data pipelines involves automating the testing, deployment, and monitoring of data pipelines for faster and more reliable updates. Data pipelines are workflows that transform raw data into valuable insights for business decisions. They typically consist of multiple stages, such as data ingestion, cleansing, transformation, analysis, and visualization. By applying CI/CD principles to data pipelines, you can achieve the following benefits: 👉 Ensure data quality and consistency across different environments 👉 Detect and fix data issues early in the development cycle 👉 Reduce manual efforts and human errors in deploying and running data pipelines 👉 Accelerate the delivery of new features and enhancements to data products 👉 Enable collaboration and feedback among data engineers, analysts, and stakeholders One of the challenges of implementing CI/CD for data pipelines is finding the right tools that support your data needs and workflows. That's where DWA tools like AnalyticsCreatorcome in handy. AnalyticsCreator is a low-code platform that helps you design, build, and manage data pipelines with ease. It supports DataOps and MLOps practices, such as: 😀 Version control and branching for managing changes to data models and code 😀 Continuous integration and delivery for deploying data pipelines to different environments 😀 Continuous monitoring and alerting for tracking data pipeline performance and health 😀 Data lineage and documentation for tracing data sources and transformations With AnalyticsCreator, you can create scalable and robust data pipelines that deliver value to your business faster and more efficiently. You can also integrate AnalyticsCreator with other tools in your CI/CD ecosystem, such as Azure DevOps or GitHub. If you want to learn more about how AnalyticsCreator can help you implement CI/CD for data pipelines, please visit our website or contact us for a demo. No-Code Data Pipeline Solution https://hubs.ly/Q02nGDbn0

As businesses grow, so does the complexity of managing data across platforms. ETL tools play a pivotal role in ensuring that data flows smoothly from diverse sources to meaningful insights. But with the variety of available tools, how do you choose one that fits your needs today and can scale with you into the future? From automation to real-time data processing, selecting the right ETL tool is about aligning technology with your goals for speed, efficiency, and flexibility. In 2024, data integration will continue to be a game-changer for competitive advantage. Explore which ETL tool can support your data journey and drive your business forward.

💡If you’re developing applications and looking for a data platform that: ✔️Maintains data immutability ✔️Removes scalability limitations ✔️Creates a single, persistent source of truth  ✔️Reduces complexity ✅ That’s Event Store. Our platform is used by companies in industries as varied as retail, manufacturing, finance, healthcare and oil & gas to serve up real-time data with rich historical context and forever-auditable data journeys for downstream analytics. Learn more about how it works ↘️ https://lnkd.in/ds9PvQm4 #EventNativeDataPlatform #EventNative #EventNative #Developers #Database #DataPlatform

Many data platforms start with a change data capture (CDC) service to extract data from an organisations transactional databases — the source of truth for their most valuable data. The idea is once you bring all that data into your data warehouse you can build whatever you need on top of that data. However, what you have built is now tightly coupled to the upstream transactional database, and that will lead to problems in the future. Over on Medium I've written about how we can remove the tight coupling caused by change data capture with data contracts. Check it out here 👇