Database On Cloud – With the advent of digital-first experiences, companies are grappling with more data than ever before. Gartner notes that 70% of organizations using cloud services today plan to increase their cloud spending to support digital experiences. They elaborated by stating “Therefore, cloud adoption is a significant means of staying ahead in a post-COVID-19 world focused on digital agility and touchpoints.”
Given the rapid adoption of cloud technologies, integration with core private clouds and on-premises operations is a critical requirement. This is where modern database replication platforms will play an important role to enable this strategy.
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Data replication is the process of copying data across different physical and virtual locations in a way where each instance of data is consistent – increasing availability and accessibility across the network. There are several technical implications and caveats of data replication including consistency compromises between final versus and strong consistency in the context of a single distributed system.
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Database replication is the process of copying data from the source database to one or more target databases. A widely used approach to database replication is to replay data manipulation language (or DML) statements from the source database to the relevant target with minimal latency to ensure data is consistent across every instance.
In a multi-instance cloud environment with endless distributed applications, file systems, storage systems, databases, and data warehouses, every enterprise strives to deploy a single source of truth to drive their analytics and operations.
But as lauded industry thought leader and database researcher Dr. Michael Stonebraker, “One size does not fit all.” . The key to supporting a wide variety of data management and analytics operations at scale is modern data replication strategies.
However, as Co-Founder and EVP of Products Alok Pareek notes in his lecture at Boston University’s Department of Computer Science, there is significant complexity in handling change flows to reliably deliver database replication for real-world use cases.
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We’ll outline how these challenges are addressed in the sections below, but first we’ll provide some context on the issue.
Reliable data replication in the first digital world is essential for every business. In retail, it can be the difference between a sale and an abandoned cart, a suspicious transaction being blocked versus a loss of thousands of dollars in fraud, and on-time delivery versus a lost package.
There are 8 main feature categories to consider when looking at modern data replication platforms to support digital first experiences and cloud adoption. The high-level goals of these features should provide the following:
One obvious example of a modern database replication architecture is the Macy implementation used for replication to Google Cloud. Macy’s approach can handle peak and holiday transaction workloads:
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The ability to mine transaction logs and transform data from applications in real-time is a core requirement of a data replication platform. While data retrieval (or CDC) changes have been around for decades, modern implementations account for new interfaces for changing flows.
This enables enterprises to build change data retrieval topologies that cater to the scale of distributed cloud platforms. ‘s ground-up, widely distributed architecture separates itself from old CDC tools that rely entirely on fragile file-based buffering.
When combining data across multiple sources into a single ‘source of truth’, modern data replication solutions can transform data with SQL constructs that data managers are already proficient in.
Here is a short list of tools in modern data stacks built to scale horizontally on commodity hardware
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However, legacy database replication tools are built to scale vertically on a single machine. This is suitable for the core vertically scaling era on on-premises proprietary machines, but doesn’t meet the needs of modern data management.
Modern database replication solutions must support multi-node deployments across the cloud to help unify your data at scale with maximum reliability.
Building to the last point – a horizontally distributed replication platform is impossible to manage without a single view that can be queried to your objects.
In the example mentioned above, Macy’s modern replication stack is built to provide end-to-end latency of under 200 milliseconds – even with Black Friday loading 7500+ transactions per second (or 19 billion transactions per month). Choosing a data replication platform that can handle peak loads and monitor latency is critical to operating at scale.
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Modern data replication tools should give users the option of relying on external transformation tools such as DBT where applicable. However, in terms of low-latency transformation requirements and custom EtLT, modern database replication must support streaming transformations. In some cases, the data needs to be formatted and modified only to comply with the target data warehouse format. In such cases ELT is not a suitable solution.
Modern data replication platforms should provide users with the ability to query data validation statistics to ensure all data read from the source application is successfully written to their target platform. Practical use cases will report on the reliability of data across the organization. If your data validation dashboard shows that your source database lags several hours behind your target data warehouse, you can immediately notify other organizations that the data may be stale when you triage issues with root causes ranging from server outages or resource crashes. data warehouse. In 2012, Amazon released its first cloud database DynamoDB and changed the database landscape forever. Since then, cloud databases have seen a rapid increase in adoption and innovation. As the entire Software development industry moves towards cloud-native development, cloud databases will be increasingly important in the coming days. Gartner has estimated that by the end of 2022, 75% of all databases will move to the Cloud:
Gartner Says The Future Of The Database Market Is CloudBy 2022, 75% of all databases will be deployed or migrated to cloud platforms, with only 5% ever being considered for… www.gartner.com
Why are cloud databases gaining popularity? In terms of database technology, public cloud databases are no different from any other SQL or NoSQL database. However, the main selling point of public cloud databases lies in database management and scaling.
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In traditional SQL databases and many NoSQL databases, the application owner manages the database, including replication, sharding, backup, restore, scaling. But in a cloud database, the cloud provider manages the database.
Although mainstream SQL and NoSQL databases are now trying to improve on these features, they were not built from the ground up for this need.
During the December 2004 Christmas sale, Amazon learned the hard way that a centralized, robust, and consistent RDBMS couldn’t handle the load of Web-scale applications. With its strict consistency model, relational structure, and 2-phase commitment, traditional SQL databases cannot provide the high availability and horizontal scalability that Amazon is looking for. The Amazon Engineering team developed a new DynamoDB NoSQL database and released their findings in their Dynamo paper in 2007. The Amazon Dynamo paper played an important role in the subsequent development of NoSQL databases such as Cassandra, Ripple.
Although DynamoDB is used as the main database for Amazon’s shopping cart application, DynamoDB was only published in 2012. Since then, DynmoDB is the most popular public cloud database and one of the most popular AWS services.
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Amazon DynamoDB is one of the most widely used large-scale cloud databases. It is also one of the most AWS services.
In recent years, there has been increasing competition from open-source databases (e.g., Cassandra, MongoDB) and other public cloud databases (Azure Cosmos DB).
Since Amazon is a leading public cloud provider, DynamoDB is still the most popular NoSQL database in the public cloud.
According to popular database ranking site DB-Engine, it is the second most popular public cloud database, just behind Azure SQL databases:
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The Stack Overflow Developer Survey 2020 has ranked DynamoDB as the 11th most popular database for 2020. This is an achievement considering that DynamoDB is the only public cloud database on that list:
Since its launch in 2012, DynamoDB is one of the trendiest Databases in the industry. The DB-Engine shows a continuous trend of growth for DynamoDB throughout its life:
There are very few companies out there that have to deal with large data sets like Google. It’s no wonder that Google is leading the BigData landscape in the 21st century with lots of new ideas and innovations. At the beginning of this century, Google found a “One size fits all” SQL database not good enough for Analytics workloads. They developed a new Database, “Dremel,” for data storage, i.e., handling large amounts of analytical data. Google published a paper, “Dremel: Interactive Analysis of Web-Scaled Datasets,” in 2010 to publish their findings.
Then, Google announced their internal Dremel Database as BigQuery Database in 2011. Since then, this Database has been at the forefront and most innovative for data storage and analytics loading. Google Cloud (GCP) has a strong presence in the Data Storage landscape, and BigQuery plays an important role there.
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BigQuery has revolutionized Data Warehousing. This is the third most popular Cloud Database according to DB-Engine ranking:
It is one of the trendiest Data Warehousing solutions and has generated a lot of hype in recent years, as shown below:
Microsoft is another big player in the database landscape. With Microsoft SQL Server, Microsoft dominates the commercial database market from
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