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Review the interesting new release database for 2017
In the world of databases, incredible news doesn’t come every week, but over the course of a year, I was surprised to see how much innovation and progress has taken place. As the editor of Database Weekly—a weekly newsletter covering new developments in databases and data storage—I often explore emerging database systems and think about what might shape the future of the field for developers.
Although there isn't a groundbreaking announcement each week, 2017 turned out to be a year full of exciting new releases. From time-series databases to multi-model solutions, several projects caught my attention. Let me walk you through some of the most interesting ones from that year.
**TimescaleDB** – A time-series database built on PostgreSQL with automatic partitioning. It extends the power of Postgres by adding optimized time-series capabilities, allowing users to query data using SQL through a special "hypertable" abstraction. This makes it ideal for applications dealing with sensor data, logs, or any time-based information.
**Microsoft Azure Cosmos DB** – Microsoft’s globally distributed, multi-model database. It evolved from DocumentDB and now offers seamless global distribution across Azure data centers. Its multi-mode support includes SQL, MongoDB, Cassandra, and even a graph API based on Gremlin. It's a powerful tool for developers who need flexibility and scalability.
**Google Cloud Spanner** – Google’s globally distributed relational database. Initially introduced in an academic paper in 2012, it's now available to the public. It supports ANSI SQL and promises high availability, making it suitable for mission-critical applications.
**Amazon Neptune** – Amazon’s graph database service designed for fast and reliable querying of connected data. It supports both Gremlin and SPARQL, making it versatile for different use cases. Neptune is perfect for applications that require complex relationship modeling, such as social networks or recommendation engines.
**YugaByte** – An open-source, cloud-native database that supports both SQL and NoSQL operations. Built in C++, it offers compatibility with Cassandra Query Language (CQL) and Redis protocols. YugaByte aims to be a stateful complement to containerized environments, offering enterprise features like monitoring and tiered storage.
**Peloton** – A self-driving SQL database that uses AI to automatically optimize performance. It also supports byte-addressable NVM storage technology. Peloton was developed as an academic project and has since become more open and accessible.
**JanusGraph** – A scalable, distributed graph database built on the foundation of TitanDB. It supports transactions, large-scale queries, and integrates well with big data platforms like Spark and Hadoop. JanusGraph is ideal for applications that rely heavily on graph relationships.
**Aurora Serverless** – AWS’s serverless version of its Aurora relational database. It allows for instant scaling and pay-as-you-go pricing, eliminating the need for manual provisioning. It's particularly useful for applications with variable workloads.
**TileDB** – A database for storing dense and sparse multidimensional arrays, commonly used in fields like genomics, medical imaging, and finance. It supports multiple compression formats and storage backends, making it flexible and efficient.
**Memgraph** – A high-performance, in-memory graph database focused on speed, scalability, and simplicity. It supports ACID transactions and uses the openCypher query language. Although not open source yet, it’s gaining traction for real-time analytics and IoT applications.
These innovations show how the database landscape continues to evolve, offering developers more tools to handle complex data challenges. Whether you're working with time-series data, graphs, or distributed systems, there's something for everyone in this ever-growing ecosystem.