Now you say that you need to install your application into 5 pc and your database is located in server and you need to access that from your local 5 pc. You simply distribute the DLLs that constitute SSCE along with your app and access the data file directly." There is no need to install a server to access an SDF database. It uses an SDF data file, which is SQL Server CE (Compact Edition) format. "A local database is one that is local to your application only. To be able to connect to a SQL Server database the SQL Server service must be running, because it's that that processes your requests and access the data file." ![]() It uses an MDF data file, which is SQL Server format. "A service-based database is a database that is only accessed through a server. But data pipelines can stream data, and therefore their load process can trigger processes in other systems or enable real-time reporting.What is the Difference between Service Database and Local Database. ETL pipelines end after loading data into the target repository. Third, data pipelines don’t have to stop after loading the data. But data pipelines can either transform data after loading it into the target system (ELT) or not transform it at all. ETL pipelines transform data before loading it into the target system. Second, data pipelines don’t have to transform the data. This supports real-time analytics and reporting and can trigger other apps and systems. But certain data pipelines can perform real-time processing with streaming computation, which allows data sets to be continuously updated. ETL pipelines usually move data to the target system in batches on a regular schedule. Below are three key differences between the two:įirst, data pipelines don’t have to run in batches. ETL pipelines are a particular type of data pipeline. Load: placing the dataset into the target system which can be an application or a database, data lakehouse, data lake or data warehouse.Īs stated above, the term “data pipeline” refers to the broad set of all processes in which data is moved between systems, even with today’s data fabric approach.Transform: converting the format or structure of the dataset to match that of the target system.Extract: pulling raw data from a source (such as a database, an XML file or a cloud platform holding data for systems such as marketing tools, CRM systems, or transactional systems). ![]() ETL is an acronym for “Extract, Transform, and Load” and describes the three stages of this pipeline: Converting raw data to match the target system before it is loaded, allows for systematic and accurate data analysis in the target repository. Batch processing enables complex analysis of large datasets.ĮTL pipelines can support use cases that can rely on historical data, and are especially appropriate for small data sets which require complex transformations. These batches can either be scheduled to occur automatically, can be triggered by a user query or by an application. Therefore, traditional batch processing where data is periodically extracted, transformed, and loaded to a target system is sufficient. ![]() Historical data is typically used in BI and data analytics to explore, analyze and gain insights on activities and information that has happened in the past.
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