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Three NoSQL Challenges That Can Be Solved with Distributed SQL

The following text is an excerpt from the new white paper, Migrating From Monolithic to Cloud Native Operational Databases

In this section, we walk through the three challenges of using current generation NoSQL databases: operational complexity, frustrating application development, and inconsistent customer experiences.

1. NoSQL operational complexity

As noted in the previous section, databases have evolved to become cloud hosted but are far from cloud native.

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Yugabyte Raises $188 Million Series C to Make Distributed SQL Ubiquitous

We are excited to announce that Yugabyte has closed $188 million in oversubscribed Series C funding. Sapphire Ventures led the round with participation from Alkeon Capital, Meritech Capital, and Wells Fargo Strategic Capital, as well as existing investors Lightspeed Venture Partners, 8VC, Dell Technologies Capital, Wipro Ventures, and others. This round comes seven months after our previous round in March 2021,

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7 Issues to Consider When Evaluating FoundationDB

FoundationDB enjoys a unique spot in the transactional NoSQL space given its positioning as a basic key-value database that can be used to build new, more application-friendly databases. Given that many of the guarantees provided by its core engine (such as multi-shard ACID transactions and high fault tolerance) are similar to those provided by YugabyteDB, our users often ask us for a comparison. These users are essentially trying to understand whether they should build their app directly using one of the three YugabyteDB APIs or should they explore/build a new database layer on FoundationDB first.

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Why are NoSQL Databases Becoming Transactional?

The NoSQL database revolution started with the publication of the Google BigTable and Amazon Dynamo papers in 2006 and 2007 respectively. These original designs focused on horizontal write scalability without compromising the performance observed in the single node databases dominant at that time. The compromises instead came either in the form of eventual consistency (i.e. inability to read the last update) or loss of multi-key access patterns (such as SQL integrity/foreign key constraints,

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Data Modeling Basics – PostgreSQL vs. Cassandra vs. MongoDB

Application developers usually spend considerable time evaluating multiple operational databases to find that one database that’s best fit for their workload needs. These needs include simplified data modeling, transactional guarantees, read/write performance, horizontal scaling and fault tolerance. Traditionally, this selection starts out with the SQL vs. NoSQL database categories because each category presents a clear set of trade-offs. High performance in terms of low latency and high throughput is usually treated as a mandatory requirement and hence is expected in any database chosen.

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Distributed Backups in Multi-Region YugabyteDB Clusters

Our post Getting Started with Distributed Backups in YugabyteDB details the core architecture powering distributed backups in YugabyteDB. It also highlights a few backup/restore operations in a single region, multi-AZ cluster. In this post, we perform distributed backups in a multi-region YugabyteDB cluster and verify that we achieve performance characteristics similar to those observed in a single region cluster.

We configured a 9 node cluster with 3 availability zones across 2 regions and repeated the benchmark introduced in the post.

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Getting Started with Distributed Backups in YugabyteDB

YugabyteDB is a distributed database with a Google Spanner-inspired strongly consistent replication architecture that is purpose-built for high availability and high performance. This architecture allows administrators to place replicas in independent fault domains, which can be either availability zones or racks in a single region or different regions altogether. These types of multi-AZ or multi-region deployments have the immediate advantage of guaranteeing organizations a higher order of resilience in the event of a zone or region failure.

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Presto on YugabyteDB: Interactive OLAP SQL Queries Made Easy

Presto is a distributed SQL query engine optimized for OLAP queries at interactive speed. It was created by Facebook and open-sourced in 2012. Since then, it has gained widespread adoption and become a tool of choice for interactive analytics. It supports standard ANSI SQL, including complex queries, aggregations, joins, and window functions. It has a connector architecture to query data from many data sources such as SQL and NoSQL databases as well as traditional big data platforms such as Hive/Hadoop.

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Are MongoDB’s ACID Transactions Ready for High Performance Applications?

Web app developers initially adopted MongoDB for its ability to model data as “schemaless” JSON documents. This was a welcome relief to many who were previously bitten by the rigid structure and schema constraints of relational databases. However, two critical concerns that have been a thorn on MongoDB’s side over the years are that of data durability and ACID transactions. MongoDB has been taking incremental steps to solve these issues leading to the recent 4.0 release with multi-document transaction support.

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YSQL Architecture: Implementing Distributed SQL in YugabyteDB

In this post, we will look at the architecture of YSQL, the PostgreSQL-compatible distributed SQL API in YugabyteDB. We will also touch on the current state of the project and the next steps in progress. Here is a quick overview:

  • YugabyteDB has a common distributed storage engine that powers both SQL and NoSQL
  • For supporting NoSQL apps, YugabyteDB is designed for low latency, sub-millisecond reads and massive write scalability. It can handle millions of requests and many TBs of data per node with linear scalability and high resilience.

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