Welcome to the inaugural edition of the YugabyteDB Community and Engineering update series! Let’s dive in and take a look at what has happened over the last few weeks.
There has been a lot of activity in terms of meetups and events. In June, YugaByte was at DockerCon. We also did a hands-on lab about building geo-distributed cloud apps at a Datariders meetup and a talk at Samsung about
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ACID transactions were a big deal when first introduced formally in the 1980s in monolithic SQL databases such as Oracle and IBM DB2. Popular distributed NoSQL databases of the past decade including Amazon DynamoDB and Apache Cassandra initially focused on “big data” use cases that did not require such guarantees and hence avoided implementing them altogether. However, ACID transactions have made a strong comeback in the last several years with the launch of next-generation distributed databases that have built-in support for them.
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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.
If you are still deciding whether or not DynamoDB is the correct choice for your use case,
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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.
DynamoDB supports a document oriented data model.
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Amazon 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. Finally, we compare and contrast alternatives such as MongoDB,
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Editor’s note – This is the first part of a two-part series on database storage engines:
When evaluating operational databases, developers building distributed cloud applications tend to focus on data modeling flexibility, consistency guarantees, linear scalability,
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In the first post of this two-part series, we learned 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, which are primarily relational/SQL in nature, support strong consistency and
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I 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 — a cloud-native, high-performance distributed SQL database platform.
YugabyteDB, which hit the 1.0 GA milestone last month,
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Results 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. This number will continue to grow in the months ahead.
SQL and NoSQL Databases on Kubernetes (source: Kubernetes Application Survey,
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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. populating the cache on reads and invalidating it on writes). This greatly increases development and operational complexity,
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