Thoughts on distributed SQL, open source and cloud native
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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Read MoreDynamoDB 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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Read MoreAmazon DynamoDB is a popular NoSQL database choice for mid-to-large enterprises. In this post, we look beyond Amazon’s marketing claims to explore how well DynamoDB satisfies the core technical requirements of fast growing geo-distributed apps with low latency reads, a common use case found in today’s enterprises. We examine the development, operational and financial consequences of working around the limitations of DynamoDB when attempting to “force-fit” for this use case.
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Read MoreWhen evaluating operational databases, developers building distributed cloud apps tend to focus on data modeling flexibility, consistency guarantees, linear scalability, fault tolerance, low latency, high throughput and easy manageability as high priority concerns. However, it is essential to have a good understanding of the underlying storage engine to reason about how the database actually delivers on these core promises. This post aims to help busy developers make informed choices in the context of database storage engines.
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Read MoreIn the first post of this series, we learnt about the B-Tree vs LSM approach to index management in operational databases. While the indexing algorithm plays a fundamental role in determining the type of storage engine needed, advanced considerations highlighted below are equally important to take into account.
Monolithic databases,
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Read MoreI am excited to announce that YugaByte has raised $16M in additional funding in a round led by Dell Technologies Capital and our previous investor Lightspeed Venture Partners. Combined with our previous funding of $8M, YugaByte has now raised $24M to solve one of the most challenging problems in operational databases today — bringing together NoSQL and SQL into a unified,
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Read MoreResults from the 2018 Kubernetes Application Usage Survey should put to rest concerns enterprise users have had around the viability of Docker containers and Kubernetes orchestration for running stateful services such as databases and message queues. Its exciting to see that nearly 40% of respondents are running databases (SQL and/or NoSQL) using Kubernetes.
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Modern user-facing apps, like E-Commerce and SaaS, frequently require features from multiple databases (broadly — SQL, NoSQL and a cache) to support their multi-workload needs. App developers are responsible for understanding and managing which pieces of data should be stored in which SQL and NoSQL database. Furthermore, the app is also responsible for moving data across the tiers (e.g.
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Read MoreTeam YugaByte is delighted to announce the general availability of YugaByte DB 1.0!
It has been an incredibly satisfying experience to, in just two years, build and launch a cloud-scale, transactional and high-performance database that’s already powering real-world production workloads. I wanted to take a moment to share our journey to 1.0 and the road ahead.
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Read MoreACID transactions are a fundamental building block when developing business-critical, user-facing applications. They simplify the complex task of ensuring data integrity while supporting highly concurrent operations. While they are taken for granted in monolithic SQL/relational DBs, distributed NoSQL/non-relational DBs either forsake them completely or support only a highly restrictive single-key flavor (see sections below). This loss of ACID properties is usually justified with a gain in performance (measured in terms of low latency and/or high throughput).
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