The YugaByte Database Blog

Thoughts on open source, cloud native and distributed databases

Distributed SQL on Google Kubernetes Engine (GKE) with YugaByte DB’s Helm Chart

The glory days of the heavy-weight hypervisor are slowly fading away, and in the last few years, containerization of applications and services is the new reality. With containerization, enterprises can prototype, deploy, and meet scale demands more quickly. To systematically and efficiently manage these large-scale deployments, enterprises have bet on technologies like Kubernetes (aka k8s), a powerful container orchestrator, to get the job done.

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AWS re:Invent 2018 Recap – The Freedom to Build

Team YugaByte was at AWS re:Invent in Las Vegas last week. While AWS was announcing a flurry of new product releases and existing product updates, we had some excellent deep dive conversations at our booth on the future of transactional databases and how YugaByte DB is playing its part in shaping that future. This post summarizes our key learnings from the conference,

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YugaByte Company and Database Update – Aug 3, 2018

$16 Million Funding Round

In case you missed the news earlier this Summer, YugaByte raised an additional $16M of funding from Dell Technologies Capital and our previous investor Lightspeed Venture Partners. With the additional funding, we are accelerating investments in engineering, sales, and customer success to scale our support for enterprises building business-critical applications in the cloud.

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New to Google Cloud Databases? 5 Areas of Confusion That You Better Be Aware of

After billions of dollars in capital expenditure and reference customers in every major vertical, Google Cloud Platform has finally emerged as a credible competitor to Amazon Web Services and Microsoft Azure when it comes to enterprise-ready cloud infrastructure. While Google Cloud’s compute and storage offerings are easier to understand, making sense of its various managed database offerings is not for the faint-hearted.

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Implementing Distributed Transactions the Google Way: Percolator vs. Spanner

Our post 6 Signs You Might be Misunderstanding ACID Transactions in Distributed Databases describes the key challenges involved in building high performance distributed transactions. Multiple open source ACID-compliant distributed databases have started building such transactions by taking inspiration from research papers published by Google. In this post, we dive deeper into Percolator and Spanner, the two Google systems behind those papers,

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How DynamoDB’s Pricing Works, Gets Expensive Quickly and the Best Alternatives

DynamoDB is AWS’s NoSQL alternative to Cassandra, primarily marketed to mid-sized and large enterprises. The uses cases best suited for DynamoDB include those that require a flexible data model, reliable performance, and the automatic scaling of throughput capacity. DynamoDB’s landing page points out that mobile, web, gaming, ad tech, and IoT are all good application types for DynamoDB.

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11 Things You Wish You Knew Before Starting with DynamoDB

DynamoDB is a fully managed NoSQL database offered by Amazon Web Services. While it works great for smaller scale applications, the limitations it poses in the context of larger scale applications are not well understood. This post aims to help developers and operations engineers understand the precise strengths and weaknesses of DynamoDB, especially when it powers a complex large-scale application.

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Polyglot Persistence vs. Multi-API/Multi-Model: Which One For Multi-Cloud?

Modern app architectures rely on data with different models and access patterns. Polyglot persistence, first introduced in 2011, states that each such data model should be powered by an independent database that is purpose-built for that model. Given the lack of horizontal scalability in RDBMS/SQL databases, the original intent was to look beyond such databases to the emerging world of NoSQL.

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Achieving Sub-ms Latencies on Large Datasets in Public Clouds

One of our users was interested to learn more about YugaByte DB’s behavior for a random read workload where the data set does not fit in RAM and queries need to read data from disk (i.e. an uncached random read workload).

The intent was to verify if YugaByte DB was designed well to handle this case with the optimal number of IOs to the disk subsystem.

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