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Ensure that data in different systems or databases are in sync and up-to-date.

Data synchronization definition:

In the context of data engineering and data pipelines, data synchronization refers to the process of ensuring that data is consistent and up to date across multiple systems or databases. This is particularly important in situations where data is being transferred or shared between different systems, such as in a data warehousing or ETL (extract, transform, load) pipeline.

Some common best practices for data synchronization include:

  • Establishing clear rules for data ownership and access permissions.
  • Ensuring that data is properly normalized and structured to facilitate synchronization.
  • Using appropriate tools and technologies to automate the synchronization process and minimize the risk of errors or inconsistencies.
  • Monitoring the synchronization process closely to ensure that any issues or discrepancies are quickly identified and resolved.

Python offers a variety of libraries and tools that can be used for data synchronization, depending on the specific use case and data sources involved. For example, tools like Apache Kafka and Apache Spark can be used for real-time data streaming and synchronization. Other tools that can be used for data synchronization in Python include SQLAlchemy, Dask, and AWS Glue.

Other data engineering terms related to
Data Management:


Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. store data for long-term retention and compliance.


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


Create a copy of data to protect against loss or corruption.


Select, organize and annotate data to make it more useful for analysis and modeling.


Identify and remove duplicate records or entries to improve data quality.


Analyzing the number of features or attributes in the data to improve performance.


Enhance data with additional information from external sources.


Extract data from a system for use in another system or application.


Create an optimized data structure for fast search and retrieval.


combine data from different sources to create a unified view for analysis or reporting.


Store the results of expensive function calls and reusing them when the same inputs occur again.


Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


Create a conceptual representation of data objects.


Track data processing metrics and system health to ensure high availability and performance.

Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.


Interpret and convert data from one format to another.


Divide data into smaller subsets for improved performance.


Transform your data so it is fit-for-purpose.


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


Increasing the capacity or performance of a system to handle more data or traffic.

Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.


Check data for completeness, accuracy, and consistency.


Maintain a history of changes to data for auditing and tracking purposes.