Sponsorship The cloud has a habit of breathing new life into a venerable, slightly dull, mature market that makes it exciting again. We’ve seen it in everything from enterprise application software to hosted storage, but perhaps the most striking turnaround is in the database. Twenty years ago, relational databases running in their own data centers or colocation centers were the norm, but that hasn’t changed for years. The cloud has since regained its sexyness in the database.
Gartner predicts that 75% of all databases will be deployed or migrated to cloud platforms by next year, down from 5%, which states it will be repatriated to on-premises systems. Tony Baer, founder and principal of technology advisory firm dbInsight, said the cloud was extensible and quickly turned into a mess.
“By putting the database in the cloud and leveraging the cloud’s native architecture, we can really rethink what the database can do,” he says. For the first time, the cloud has enabled companies to scale out rather than scale up, achieving linear scalability. This opens up new possibilities as companies consume more and more data.
Gartner’s 2020 cloud database Magic Quadrant estimated that $ 17 billion (31%) of global DBMS market revenue of $ 55.4 billion came from the cloud. The cloud database market is showing no signs of slowing, with 70% of DBMS market growth coming from cloud databases in 2019. Still, Gartner said it will contribute to half of the overall market growth in 2023.
Challenger and foresight
The Gartner quadrant categorizes market players into niche players (players with limited market appeal), visionaries, challengers, and leaders. Gartner considers only two companies to be challengers, and they are unlikely to be friends.
With an open source database, Redis Labs has three advantages. First, it targets multiple cloud providers and offers customers the option of public cloud infrastructure. Second, it provides multi-model support. This means that you can cover everything from key-value stores to text and geolocation models to a single backend engine, not just the traditional relational model. Finally, it has made a strong name for itself in the in-memory database market and has become the go-to tool for low-latency use cases such as real-time fraud detection.
Another company in Challenger Space is Snowflake. For companies less than 10 years old, it has risen tremendously, culminating in its September IPO, and for some time its market capitalization has surpassed IBM. This is one of a new generation of young database companies that Bear likens to an “empty data mart.” Launched with a focus on cloud data warehouses, the company is looking to become a data sharing market where companies can publish data to each other in a read-only format.
If it hadn’t grown that much, Snowflake could have landed in a visionary category reserved for small, agile businesses with a high degree of innovation. Here you can find Snowflake’s rival Databricks. It focuses on combining large, vast unstructured data lakes with smaller, more sophisticated data collections called “data lake houses.” It also plans an IPO this year.
It’s worth noting that there are two and four players in each of these two quadrants, but eight in the leader quadrant. Leaders are well-established players with support for a wide range of use cases, mature products, and support for hybrid and cloud-native deployment models. This shows that despite the bubbling nature of the cloud database market, there are large, well-established companies with a deep technology stack and wide coverage that maintain their competitive advantage.
People in the leader category are usually low-risk options, but that doesn’t mean they aren’t innovators. These companies have a solid sense of what will happen next.
Some of these companies come from the background of traditional pre-cloud enterprises that are embracing the cloud with either solutions designed to run on other people’s infrastructure or their own cloud services. I am. While SAP is in the leader quadrant for operational and analytics services, Oracle has also made a leap to the cloud with its own infrastructure or Autonomous Database products hosted at customer facilities.
Mega vendors’ attempts to win the cloud database market remind us of the difficult situation more than a decade ago when they persuaded people to buy an Exadata hardware / software combination.
“The core client didn’t use Exadata right away,” he says. “It took years to prove itself and say that doing this database integration is actually feasible.”
Not all cloud service providers fall into the leader category (for Tencent and Alibaba), but other heavy hitters do.
IBM is in the Leaders quadrant of Gartner, but is close to the bottom of the region in terms of execution capabilities. Baer says the company’s historic fear that cloud databases prey on mainframe businesses has hampered it.
That said, IBM has performed some impressive analytical work in hybrid cloud environments and is using Cloud Pak for Data products to demonstrate leadership among cloud service providers across multi-cloud environments. This binds the tool and manages the data lifecycle, such as data virtualization tools that read from multiple sources. It also has support for both its own Db2 Warehouse and MongoDB. It is available on multiple other CSP platforms.
As expected, the top three cloud database providers are the top three service providers. Microsoft adopted the concept of cloud databases early on and gradually embodied its products with a multifaceted approach. In addition to the cloud-native version of the SQL Server database, we also provide Cosmos DB and MongoDB, which support the API for the Apache Cassandra database originally created by Facebook. We also released a cloud-native managed Cassandra instance in March. It also has a wealth of analysis functions.
Over the past few years, Google has been busy entering the cloud database market with Spanner-managed relational databases and products such as Datastore, Firestone, and BigTable, NoSQL products that provide key-value and document storage. The company also has BigQuery, a data warehousing product that offers across other cloud platforms in the form of BigQuery Omni. This is a sign of cloud database space movement as CSPs are starting to offer in-house developed databases on other cloud platforms.
The rise of AWS
Next is Amazon Web Services (AWS), the grandfather of the cloud services database platform. The platform was ranked number one among Gartner’s Magic Quadrant leaders. This shows that the company’s market advantage is an important advantage.
As a cloud service provider launched a few years ago by a major competitor, AWS has made a good start in developing database systems. After deploying Amazon Relational Database Service (RDS), the first database service to manage a collection of cloud-based relational systems, in 2009, we launched a scale-out DynamoDB key-value database in 2012. In the same year, it was developed in the same way. From the Amazon Redshift data warehouse.
Over time, the company embodied database products with a variety of homemade products, including the ElastiCache in-memory database (2011), Aurora RDBMS (2014), and Neptune graph database (2018). AWS deployed the Quantum Ledger Database (QLDB) in 2018 to help customers build ledger-like applications, and the DocumentDB NoSQL database in 2019, which also supports MongoDB workloads.
AWS is keeping pace with the development of its managed database portfolio, last year launching Timestream, a time-series database for workloads including IoT, and an Apache Cassandra-compatible wide-column database called Keyspaces.
What’s Next for the Cloud Database Market? Amazon leads the pack in terms of the number of different managed database types offered. Baer hopes to shift towards layering more functionality on top of the raw database engine. He predicts that this change in momentum will take cloud service providers in a new direction.
“One is to fill out these data platforms. The other is to bring together more consistent cloud services and build integrations with other cloud services such as AutoML. [automated machine learning], Or visualization. “Data pipeline automation will also be the focus, he says. This has already been confirmed by enhancements to projects such as AWS Glue, a data integration…