Spark SQL is a library within the Apache Spark project that allows for the processing of structured data. It provides a unified interface to access a variety of data sources, including HDFS, Hive, JSON, and Cassandra. It enables users to query data using SQL, as well as the more traditional Spark APIs. It also provides support for in-memory computing and advanced analytics, such as machine learning and graph processing. Spark SQL is designed to be highly scalable and efficient, allowing for the processing of large datasets in a distributed manner.
Data lineage is the process of tracking the origin, flow and current state of data. It requires an understanding of processes and the entities and databases that interact with the data, as well as how that data is transformed as it is passed along these processes. Data lineage is used to identify an issue within the data flow, or to map data sources to where it is used downstream. It can also be used for impact analysis and understanding how and why data changes, or for compliance and auditing purposes. Data lineage can provide analytical output helpful for data quality, security and data governance. It is an important part of data management and analytics.
Data lineage for Spark SQL provides the benefit of traceability and visibility of datasets, enabling users to quickly analyze data lineage to ensure data accuracy and trustworthiness of critical decisions. It also promotes high-quality data governance by tracking the origin and influence of input, intermediate and output data. Additionally, it allows users to gain a better understanding of data flow and analysis to improve data mining and machine learning tasks.
Setting up data lineage using Spark SQL and Secoda requires a few simple steps at first. Firstly, the input data is ingested and loaded into Spark while in the meantime the data is being transformed into a suitable format for Secoda’s automated data lineage. Secondly, Secoda is used to read the source of the input data and monitor the data transformation process. Lastly, the output converted data is verified using Secoda and stored into the system for later reuse.
Secoda is a data discovery tool for the modern data stack that helps organizations unlock the value of their data. It enables users to quickly and easily identify, explore, and visualize data from across their entire data stack, including databases, data warehouses, data lakes, and cloud services. Secoda provides an intuitive interface for users to explore data, discover insights, and create visualizations. It also offers powerful features such as data lineage, data profiling, and data quality monitoring to ensure data accuracy and trustworthiness. With Secoda, organizations can gain a comprehensive understanding of their data and unlock the full potential of their data assets.