Welcome to the IOblend blog page. We are the creators of the IOblend real-time data integration and advanced DataOps solution.
Over the many (many!) years, we have gained experience and insight from the world of data, especially in the data engineering and data management areas. Data challenges are everywhere and happen daily. We are sure, most of you, data folks, are well versed in them. In fact, we will venture to say that you spend over three quarters of your time dealing with them.
You encounter data challenges when doing system integrations, cloud/prem/edge dataflow development, analytical dashboards implementation, master data services creation, data warehousing projects, etc. Throw in various systems, various stakeholders and tech from different eras, all contributing to your data headaches. Then add to the hassles the overbearing red tape and a heavy-handed procurement and you got yourself an enterprise-grade pile of tech and processes that are truly hard to get a handle on. If you needed to start a new large-scale data project in that environment? Well, it will likely be a daunting undertaking…
Most of these challenges are caused by the cumbersome efforts with data engineering and data management. Think about it, these initiatives include data, or rather flows of data from the source to destination (and transformations in between). If you are unable to do solid data engineering in all your projects, bad data issues inevitably unravel later. Bad data means bad decisions. You absolutely have to get the dataflow design and oversight right, but that is the tricky part – data engineering and data management are hard and resource-consuming.
Ideally, you should implement DataOps, which is the concept that unites best practice data engineering and data management under one umbrella. It is by far the best approach to eliminate data issues and give you the most robust data estate, but DataOps too is a high effort job, requiring skilled engineers to deliver it.
If only there were a simple tool that could make DataOps a ‘walk in the park’
There had to be a better way to work with data and data estates, where we could deliver robust data to your organisations and empower your data citizens to work with very complex data management techniques without necessarily having advanced knowledge of data engineering concepts.Â
We did find that way, in case you were wondering, and you can read more about it here.
What we want to do in this section is to share some of the best practice, tips and tricks, or just cool ways of doing things with DataOps (and our platform, naturally). We want to show you a different perspective on doing things you do every day simpler and better. But we do not want to make the blog overly taxing to digest or deeply technical (that would defeat the whole purpose of what we are promoting!)
We strongly believe solid data engineering and management foundations are the way of the future when it comes to data management practices, especially relevant when working with Big Data, IoT, AI/ML and operational analytics applications. If you have data flowing through your systems, apps, dashboards, etc., we urge you to explore the power of IOblend DataOps. You will be surprised why you haven’t done it earlier.
Stay well and safe and watch this space for updates!
DB2 CDC to Lakehouse Without Re-Platforming
From DB2 to Lakehouse: Real-Time CDC Without Re-Platforming 💻 Did you know? Mainframe systems like DB2 still process approximately 30 billion business transactions every single day. Despite the rush toward modern cloud architectures, the world’s most critical financial and logistical data often resides in these “legacy” environments, making them the silent engines of the global economy.Â
Real-Time Upserts: Deduping and Idempotency
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Streaming Data Quality That Won’t Break Pipelines
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Schema Drift: The Silent Killer of Data Pipelines
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Preventing Data Drift in Modern Data Systems
The Invisible Erosion: Detecting and Managing Data Drift in Modern Architectures 📊 Did you know? According to recent industry surveys, over 70% of organisations experience significant data drift within the first six months of deploying a production system. The Concept of Data Drift Data drift occurs when the statistical properties or the underlying structure of incoming data change
Stream Database Changes to Your Lakehouse with CDC
Zero-Lag Operations: Stream Database Changes to Your Lakehouse 💾 Did you know? The “data downtime” caused by traditional batch processing costs the average enterprise approximately ÂŁ12,000 per minute. The Concept: Moving at the Speed of Change Zero-lag operations rely on a transition from periodic “snapshots” to continuous “streams.” Instead of moving massive blocks of data at
