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Designing A Non-Relational Database Engine

Data Engineering Podcast

The default association with the term "database" is relational engines, but non-relational engines are also used quite widely. In this episode Oren Eini, CEO and creator of RavenDB, explores the nuances of relational vs. non-relational engines, and the strategies for designing a non-relational database.

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Building Transactional Systems Using Apache Kafka

Confluent

Traditional relational database systems are ubiquitous in software systems. They are surrounded by a strong ecosystem of tools, such as object-relational mappers and schema migration helpers. A tomicity in relational databases ensures that a transaction either succeeds or fails as a whole.

Kafka 22
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Readings in Streaming Database Systems

Confluent

What will the next important category of databases look like? For decades, relational databases were the undisputed home of data. They powered everything: from websites to analytics, from customer data […].

Database 121
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Why Open Table Format Architecture is Essential for Modern Data Systems

phData: Data Engineering

The world we live in today presents larger datasets, more complex data, and diverse needs, all of which call for efficient, scalable data systems. These systems are built on open standards and offer immense analytical and transactional processing flexibility. These formats are transforming how organizations manage large datasets.

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Data Integrity for AI: What’s Old is New Again

Precisely

The simple idea was, hey how can we get more value from the transactional data in our operational systems spanning finance, sales, customer relationship management, and other siloed functions. There was no easy way to consolidate and analyze this data to more effectively manage our business. But simply moving the data wasnt enough.

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Simplify Delta Lake Complexity with mack.

Confessions of a Data Guy

Anyone who’s been roaming around the forest of Data Engineering has probably run into many of the newish tools that have been growing rapidly around the concepts of Data Warehouses, Data Lakes, and Lake Houses … the merging of the old relational database functionality with TB and PB level cloud-based file storage systems.

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How Apache Iceberg Is Changing the Face of Data Lakes

Snowflake

Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew. The data warehouse solved for performance and scale but, much like the databases that preceded it, relied on proprietary formats to build vertically integrated systems.