Hive Partition
I am a data engineer who is responsible for designing, building, maintaining, and testing the infrastructure and systems that are used to store, process, and analyze data. I work closely with data scientists and analysts to ensure that the data pipelines and systems are able to support the data needs of an organization.
I have a strong background in computer science and software engineering, and skilled in programming languages such as Python, Java, and SQL also familiar with database systems and big data technologies like Hadoop, Spark, and NoSQL databases.
Some of my key responsibilities as a data engineer:
Designing and building data pipelines to extract, transform, and load data from various sources Setting up and maintaining data storage and processing systems, including data warehouses and data lakes Collaborating with data scientists and analysts to understand their data needs and ensure that the data infrastructure can support their requirements Performing data quality checks and troubleshooting any issues that arise Implementing security and privacy measures to protect sensitive data
Hive Partition
For efficient data analysis and loading we need partition in Hive, not only hive partition concept can be applicable in all other data platforms.
In Hive we can load only structured data, in that we can choose partitions in available columns, that columns can be chosen by below criteria,
- Should not be Primary key like column,
- It should be less uniqueness,
- frequently used column for filter.
There are two type of partition method available in Hive,
Static Partition
In static partition we can specify the partition value while loading and so it will be little bit faster.
Dynamic Partition
Here hive will find the distinct values for partition from partition column.
Hive support multi level partitions too.
