What methods and approaches exist for moving large data arrays between database systems?

The following methods and approaches are used for transferring large datasets between database systems:

1. Export/Import via files — data is exported to an intermediate format (CSV, JSON, XML, SQL dump), then imported into the target database.
2. Using ETL tools (Extract, Transform, Load) — specialized programs (e.g., Apache NiFi, Talend, Pentaho) that allow extracting, transforming, and loading data.
3. Replication and synchronization — setting up replication mechanisms between databases if they support such features.
4. Batch processing with scripts — writing scripts in Python using database libraries (e.g., psycopg2, pyodbc) that read data in chunks and write to another database.
5. Using specialized protocols and APIs — if databases support data exchange via REST API or other protocols.

Example in Python using batch data loading from one database to another:

import psycopg2

source_conn = psycopg2.connect(...)
dest_conn = psycopg2.connect(...)

with source_conn.cursor() as src_cur, dest_conn.cursor() as dst_cur:
    src_cur.execute('SELECT * FROM large_table')
    while True:
        rows = src_cur.fetchmany(1000)  # read 1000 rows at a time
        if not rows:
            break
        args_str = ','.join(dst_cur.mogrify('(%s,%s,%s)', row).decode('utf-8') for row in rows)
        dst_cur.execute('INSERT INTO large_table VALUES ' + args_str)
    dest_conn.commit()

This approach helps avoid loading the entire table into memory and efficiently transfers large volumes of data.