Method 1: SQL Server Streaming Using Apache Kafka Image Source: Apache Kafka is a distributed data store that is primarily used for ingesting and analysing streaming data. Methods To Implement Real-Time Data Streaming In SQL Serverĭata can be streamed from the SQL Server in real-time using the following two methods: Real-time data applications, therefore, leverage stream processing in its architecture. The latency is almost unnoticeable as the data is processed within seconds or milliseconds. This results in small-sized data, containing individual records or only a few records. Stream processing takes an alternative approach in which data processing is limited to the most recent or over a rolling time-window. The Hadoop MapReduce framework is the best way to process data in batches. This data processing method inadvertently leads to latency which can vary from minutes to hours depending on the size of the data being processed. This is usually done on the entire data or over most of the data. Batch processing requires data to be queried in chunks. A client machine to set up Debezium and Kafka connect API.Ĭomparing Batch & Stream Processing Image Source: Initially, data was always processed using the concept of batch processing.Get started with hevo for free Prerequisites Hevo’s pre-built integration with SQL Server ( among 100+ Sources) will take full charge of the data replication process, allowing you to focus on key business activities. It helps you replicate the data effortlessly from SQL Server without any intervention. Hevo Data provides a hassle-free & a fully managed solution using its No-code Data Pipelines. Method 2: Using Hevo, The Easier Approach This method makes use of custom scripts written by the user to stream data from the SQL Server by enabling change data capture feature. Kafka connector API along with debezium can implement SQL Server streaming. It provides a unified data platform that helps to develop real-time data pipelines and applications. Method 1: SQL Server Streaming Using Apache KafkaĪpache Kafka is a distributed data store that is primarily used for ingesting and analysing streaming data. Some common examples of streaming data are log files generated by a web application, financial data from stock trading, location data from GPS applications, data from sensors in industrial equipment, etc. This allows them to understand the trends and performance of their product in the market. This means that the data flows in continuous streams that have no specified beginning or end but rather provide the opportunity for the data to be acted upon in real-time.Īnalyzing streaming data can help organizations gain insights about various metrics such as server activity, website clicks, Geo-location of users, etc. It is simultaneously transferred usually in small sizes (order of kilobytes) to be processed, analyzed in a sequential fashion. Introduction To Streaming Data Image Source: Streaming data can be defined as the data that is generated continuously from a wide variety of sources. Threading: MS SQL efficiently supports multi-threading & parallel processing even with a massive amount of data & ensures powerful analytics.įor further information, you can check the Microsoft site on SQL Server here.Integrations: It is very easy to integrate MS SQL with tools like Hadoop for big-data analytics using T-SQL commands.It uses languages like Python or R to perform such operations. Analytics Support: MS SQL supports data analytics & machine learning.It further supports data recovery during crashes/failures. Secure: It ensures data security & availability irrespective of whether the data is at rest or being worked on.Performance: It performs exceptionally well on both Windows & Linux.Scalable: It is easy to scale and supports large amounts of data.Initially, SQL Server ran only on Windows Server and Windows devices, however now it is supported on Linux as well. SQL Server is tied to Transact-SQL or T-SQL (Microsoft propriety language) for its programming interface, such as declaring the variable, stored procedure, exception handling, etc. SQL Server is built on top of SQL (Structured Query Language) to interact with database objects. Introduction To SQL Server Image Source: It is a relational database developed by Microsoft to store structured data. Method 2: Using Hevo, The Easier Approach.The article begins with an introduction to the high-level concept of streaming data and then shifts to the practical implementation of SQL Server streaming data. Real-time data streaming from SQL Server will help you make fast, data-driven decisions on your transactional data. Method 2: Using Hevo Data, The Easier Approach. Method 1: SQL Server Streaming Using Apache Kafka.Methods To Implement Real-Time Data Streaming In SQL Server.
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