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Review the interesting new release database for 2017
In the ever-evolving world of databases, there isn’t always groundbreaking news every week. However, over the course of a year, it's surprising to see how many new developments and innovations have emerged in this field. As the editor of Database Weekly—a weekly newsletter covering the latest in databases and data storage—I often find myself exploring emerging database systems to understand what ideas might shape the next few decades. These advancements directly impact everyday developers.
Although the database space may not be full of daily surprises, 2017 was still packed with exciting updates. I wanted to take a moment to highlight some of the most interesting new releases, including a transactional graph database, a geographically replicable multi-model database, and a high-performance key-value store.
**TimescaleDB** – A time-series database built on PostgreSQL with automatic partitioning
One of the most exciting new projects to come out of the PostgreSQL ecosystem is TimescaleDB. This Apache 2.0 licensed extension was developed by a team of PhDs and brings time-series capabilities to PostgreSQL through automatic partitioning. It integrates seamlessly with the standard PostgreSQL interface and tools, allowing users to query time-series data using a "hypertable" abstraction, which functions like a regular SQL table.
**Microsoft Azure Cosmos DB** – A globally distributed, multi-model database
Cosmos DB is the rebranded version of Azure DocumentDB, now offering global distribution across Azure’s data centers. One of its standout features is its ability to automatically route requests to the nearest region containing the required data, without requiring any configuration changes. The "multi-mode" aspect allows users to interact with the database through multiple APIs, including SQL, MongoDB, Cassandra, and even a Gremlin-based graph API.
**Google Cloud Spanner** – A globally distributed relational database
Google’s Cloud Spanner has been under development since 2007 and was first publicly discussed in an academic paper in 2012. Designed for global scalability and high availability, it offers enterprise-grade security and 99.999% uptime. It supports ANSI SQL 2011, making it a powerful relational database solution that is both familiar and robust.
**Amazon Neptune** – A fully managed graph database service
Amazon introduced Neptune at their re:Invent conference, offering a fast and reliable graph database service. It supports two major query languages: Gremlin (a widely adopted standard) and SPARQL (for RDF-based graphs). Neptune is designed to simplify the deployment of graph databases while providing the necessary performance and reliability.
**YugaByte** – An open-source cloud-native database
YugaByte stands out for its support of both SQL and NoSQL modes, making it ideal for cloud-native applications. Built in C++, it supports Cassandra Query Language (CQL), Redis protocol, and is working on PostgreSQL compatibility. It also integrates with Spark and offers enterprise features such as monitoring, alerting, and tiered storage.
**Peloton** – A self-driving SQL DBMS
Peloton explores the use of AI to autonomously optimize database performance. It supports byte-addressable NVM storage and is open-sourced under the Apache license. Its goal is to create a self-managing database that can predict workload trends and adjust accordingly without human intervention.
**JanusGraph** – A scalable, distributed graph database
Based on TitanDB, JanusGraph is a ready-to-use graph database optimized for large-scale deployments. It supports transactions, high concurrency, and integrates with big data platforms like Spark and Hadoop. It can also leverage various storage backends, including Cassandra, HBase, and Google Cloud Bigtable.
**Aurora Serverless** – A pay-as-you-go relational database on AWS
At the re:Invent conference, Amazon announced Aurora Serverless, a serverless version of its popular Aurora database. It allows users to pay only for the compute time they use, eliminating the need for manual scaling. While still in preview, it promises to bring more flexibility and cost efficiency to database management.
**TileDB** – A database for dense and sparse array data
Developed by MIT and Intel, TileDB is designed for storing multidimensional arrays, which are common in fields like genomics, medical imaging, and financial analytics. It supports various compression formats and can store data in distributed file systems like HDFS or S3.
**Memgraph** – A high-performance in-memory graph database
Memgraph focuses on speed, scalability, and simplicity, aiming to provide fast analysis of interconnected data from IoT devices and other sources. It uses in-memory ACID transactions and supports persistent disk storage, though it is not yet open source.
Overall, 2017 was a year of innovation and growth in the database landscape, with several promising technologies emerging that could shape the future of data management.