Articles

NoSQL Migration Essentials

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Need help with your NoSQL migration? Look no further than our « NoSQL Migration Essentials » Refcard. We walk through the primary steps for moving out of a relational database, plus important design principles to understand and consider in your migration process.

Readers will review key concepts that range from denormalizing and modeling data to defining access patterns, designing primary keys and indexes, and creating an entity relationship diagram — all demonstrated with a simple site application example. As a bonus, readers can use the included JSON structure at the end to interact with a NoSQL playground.
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Why Mutability Is Essential for Real-Time Data Analytics

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Successful data-driven companies like Uber, Facebook, and Amazon rely on real-time analytics. Personalizing customer experiences for e-commerce, managing fleets and supply chains, and automating internal operations require instant insights into the freshest data.

To deliver real-time analytics, companies need a modern technology infrastructure that includes three things:

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How To Use Cluster Mesh for Multi-Region Kubernetes Pod Communication

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Thanks to services provided by AWS, GCP, and Azure it’s become relatively easy to develop applications that span multiple regions. This is great because slow apps kill businesses. There is one common problem with these applications: they are not supported by multi-region database architecture.

In this blog, I will provide a solution for the problem of getting Kubernetes pods to talk to each other in multi-region deployments.

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Debunking Four Metadata Myths

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Data is being accumulated at an alarming rate due to the growth of unstructured data. Within that is metadata, which allows us to quickly find data files by identifying certain properties. It wasn’t until recently that developers had to pay closer attention due to the overwhelming effect it was having on our data engines. Metadata used to just be a thing that could be stored in memory behind the scenes without any worry, but its expansion presents the growing concern of where to store it and how to manage it.

Every database system, whether SQL or NoSQL, uses a data engine (aka storage engine), embedded or not, to manage how data is stored. Our ever-growing volume of data has outgrown our data engines’ infrastructures and with that, they are stretched to their limits and forced to make painful trade-offs necessary to support the scale and performance requirements of modern datasets.

Source de l’article sur DZONE

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How to Mutate Data in a System Designed for Immutable Data

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In a post published on our blog earlier this year, we described some of the decision-making that went into the design and architecture of Snuba, the primary storage and query service for Sentry’s event data. This project started out of necessity; months earlier, we discovered that the time and effort required to continuously scale our existing PostgreSQL-based solution for indexing event data was becoming an unsustainable burden.

Sentry’s growth led to increased write and read load on our databases, and, even after countless rounds of query and index optimizations, we felt that our databases were always a hair’s breadth from the next performance tipping point or query planner meltdown. Increased write load also led to increased storage requirements (if you’re doing more writes, you’re going to need more places to put them), and we were running what felt like an inordinate number of servers with a lot of disks for the data they were responsible for storing. We knew that something had to change.

Source de l’article sur DZONE

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