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Category: MongoDB

Distributed SQL Summit Recap: How Admiral Scales Globally and Achieves Single Digit Latency

Distributed SQL Summit Recap: How Admiral Scales Globally and Achieves Single Digit Latency

At the Distributed SQL Summit 2020, James Hartig – Co-Founder at Admiral, presented the talk “How Admiral Scales Globally with YugabyteDB on Google Cloud While Maintaining Single-Digit Latency.”

Admiral’s Go application runs in Google Cloud across 5 regions in 3 continents. This geo-distributed architecture is powered by a single YugabyteDB cluster that delivers an average global read latency of 3ms! In this talk,

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How to Migrate the Sakila Database from MongoDB to Distributed SQL with Studio 3T

How to Migrate the Sakila Database from MongoDB to Distributed SQL with Studio 3T

Whether it’s consolidation, or modernizing your data infrastructure, data migration is a serious undertaking. Without sufficient planning, countless hours will be spent constructing the proper schema in the target database, and picking the proper ETL tools to help move the data. In this blog we will show you how to migrate the Sakila demo database from MongoDB to a YugabyteDB cluster using the Studio 3T tool.

Why migrate to a distributed SQL database from MongoDB?

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How to Achieve High Availability, Low Latency & GDPR Compliance in a Distributed SQL Database

How to Achieve High Availability, Low Latency & GDPR Compliance in a Distributed SQL Database

Today’s developers understand that the key requirement to converting and retaining customers is all about delivering fast and responsive experiences, while remaining resilient to failures and compliant with data governance regulations. YugabyteDB is purpose built for geo-distributed applications that require high availability, high performance and regulatory compliance. In this blog, we are going to “look under the hood,” to explore exactly how YugabyteDB distributes data across multiple clouds, regions and availability zones.

YugabyteDB is an open source,

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YugabyteDB High Availability & Transactions for PostgreSQL & MongoDB Developers

YugabyteDB High Availability & Transactions for PostgreSQL & MongoDB Developers

In the first post of our series comparing YugabyteDB with PostgreSQL and MongoDB, we mapped the core concepts in YugabyteDB to the two popular databases. This post is a deeper dive into the high availability and transactions architecture of these databases.

High Availability

Almost all databases including YugabyteDB use replication to ensure that the database remains highly available under failures. The basic idea is to keep copies of data on independent failure domains so that loss of one domain does not lead to data loss or data unavailability from the application client standpoint.

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Mapping YugabyteDB Concepts to PostgreSQL and MongoDB

Mapping YugabyteDB Concepts to PostgreSQL and MongoDB

If you are developing a new distributed application or are extending an existing one with a new set of microservices, chances are you are going to need to store data in a distributed SQL database. The plethora of niche databases that have emerged over the last decade make the task of selecting a database challenging. With many databases, each with its own nomenclature and nuances to choose from, learning a new database can be a daunting task.

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Why are NoSQL Databases Becoming Transactional?

Why are NoSQL Databases Becoming Transactional?

The SQL vs. NoSQL database split 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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Are MongoDB’s ACID Transactions Ready for High Performance Applications?

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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YugabyteDB 1.1 New Feature: Document Data Modeling with the JSON Data Type

YugabyteDB 1.1 New Feature: Document Data Modeling with the JSON Data Type

Welcome to another post in our ongoing series that highlights new features from the latest 1.1 release announced last week. Today we are going to look at document data modeling using the native JSON data type available in YugabyteDB’s Cassandra compatible YCQL API. Note that this data type is specific to YugabyteDB and is not part of the standard Cassandra Query Language (CQL).

With YugabyteDB’s native JSON support,

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