Materialize- The Streaming Database on SQL Platform


Materialize is a streaming database that enables to do incremental computation without complications in data pipelines. Materialize is the SQL platform used for processing streaming data. With lots of technical research behind it, materialize was launched in 2019 to build real-time applications easily and efficiently on streaming data so that businesses can get actionable intelligence from streaming data.

Materialize was built with SQL interface and with prominence on comprehensive SQL surface area, correctness, and efficiency. Materialize was built under two systems named Timely data flow and differential data flow which was under development since 2013. Materialize is living under the front end of the data pipeline, it has the ability to maintain historical content in a summarized manner.

There is an explosion of streaming data from many sources such as social media, mobile devices, and the internet of things. The data produced are short-lived but have highly valuable insights. Business needs to track this data to create actionable intelligence and also to develop real-time insights from streaming data.

Realtime data and historical data together provide critical insights which allow the business to forecast their requirements and can accurately monitor the rapid changes in the market condition. With this new technology, businesses can build new products, deliver better customer experiences and reduce costs.


Digital transformation is forcing many enterprises to purchase streaming data for real-time insights. It is easier and it can be used for long-term survival. The streaming data can be broadly divided into 5 stages

  • Building the foundation of the streaming data framework.
  • Real-time dashboards
  • Streaming microservices
  • Predictive applications 
  • Automated processors

Materialize has a light footprint, so it can be integrated on a single computer. It is designed as a parallel data computing platform that connects low latency operations with high reactive computations. Materialize executes as the single binary process called materialized which is supported on Linux X86-64 platforms.

Materialized can be connected to sources such as Kafka or CDC from relational sources and files.


Materialize is built with several key principles such as


SQL is the industry standard matured language and it is used for managing data. SQL is the easiest and fastest language to develop applications on streaming data.


It always provides the guaranteed and correct query answers. 


Materialize helps easy integration compatible with PostGres, it allows rapid integration and iteration.


Materialize is well equipped to maintain the indexed presentation of data when building new collections. It provides high performance, scalability, and efficiency at a low cost.


The execution plans created by materializing are called dataflows.  An independent component present in dataflows describes the connection between the components. All components are operated in addition to the response of input data. By showing this as a computation on streams it explains how it should respond on static input data sets and how it should respond to new data which comes later on.

A data flow is also unique in its ability to show  higher-level control constructs like iterations, that allows it to execute SQL statements such as Recursive CTE.

When materialize starts executing its plan then individual threads are operated on components of the dataflow independently and concurrently which allows each dataflow to horizontally across multiple work threads. The new data flows created are multiplexed over the same workers. So you need not force as many resources as you have any queries.

Dataflow’s components are data-parallel and can be automatically parallelized and multiplexed across multiple threads,  and computers for scalability and efficiency. 

Materialize Timestamp-Based Concurrency Control 

A complete disorder is created when multiple workers cooperate on a fast-moving computation and they run at high risk. The use of a traditional database mechanism for preventing this issue will affect the throughput and efficiency. Materialize uses the two streams of shared states one is fine-grained sharing using locks and coarser sharing using copies.

These shared states are shared only within each worker. Materialize has a logical timestamp that indicates when the updates take place. This timestamp is maintained automatically and allows materialization to use MVCC to both shared indexed states between the data flows.


The companies need to learn and adapt new technology because the enterprise business landscape is going high. The growth of the companies depends upon the customer experiences and profitability. All these are achieved with modern data frameworks.

Materialize provides an easy way to deploy a streaming data processing platform and the necessary tooling for real-time dashboards, Microservices, business process automation, and predictive decision-making. The developers can create real-time applications quickly and efficiently, and enables a business to move from reactive decision making to proactive decision-making.

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