Problem Statement:

If your organization supports multiple apps or platforms, it’s common for each to use a different database — for example, one application might use MongoDB while another relies on PostgreSQL.

When you need comprehensive analytics that span multiple systems, you’ll often face the challenge of joining tables or datasets that reside in separate databases. To gain true organizational insights, your analytics solution should offer robust data integration capabilities.

Read on to discover how modern platforms achieve unified analytics from diverse sources.

At chitr.io, our platform features a robust inbuilt Federated Query Engine, allowing you to access and analyze data across multiple sources in real time—without the traditional complexity of manual integration. If your project requires advanced data transformation, we also offer agile ETL services that can be deployed as needed, minimizing operational overhead and ensuring truly seamless data integration for every client scenario.

Solutions:

1. Using a Federated Query Engine (e.g., Trino/Presto)

How it works:

Deploy a federated SQL engine like Trino (formerly Presto) which connects to multiple databases as “catalogs.” Trino allows you to write SQL queries that join tables across any supported source (e.g., MySQL + PostgreSQL + MongoDB). Your Analytics solution can connect to Trino as a single datasource, and the cross-database logic occurs inside Trino, not your solution.

Steps:

• Set up a Trino server and connect it to your required databases as catalogs.

• Connect your Analytics Solution to Trino using a connector.

• Use standard SQL queries in analytics solution, referencing tables using Trino’s catalog.schema.table syntax, enabling joins across different databases.

Pros:

• True cross-database joins, central query access, and massive flexibility across disparate data sources.

• Works in near real-time for analytics use-cases.

Cons:

• Additional infrastructure and operational complexity (requires deploying and managing Trino).

• Requires SQL knowledge for query authors.

• Performance depends on network, data volume, and Trino cluster resources.

2. ETL/ELT Approach: Data Warehousing

How it works:

ETL pipelines move/copy data from multiple sources into a single relational data warehouse (e.g., PostgreSQL, Redshift, BigQuery). Your Analytics Solution can then connect to this warehouse, allowing native joins.

Steps:

• Build an ETL/ELT process to extract, transform, and load data into a unified warehouse from all source systems.

• Have your analytics solution to pull data from the Unified warehouse

Pros:

• Maximizes query performance by pushing all computation into a high-speed warehouse.

• Data integrity and governance can be more easily enforced.

• You can selectively load only the data needed for analysis, rather than entire datasets. By transforming large volumes of raw data and filtering for just the relevant records and fields, your analytics processes remain efficient and lightweight — minimizing both storage impact and resource usage.

Cons:

• Requires maintaining ETL jobs and storage, leading to possible data staleness (not always real-time).

• Upfront design effort.

• Higher storage costs if not transforming to reduce data.

3. Database Link/View Techniques (Advanced)

How it works:

Some RDBMS support direct database links, allowing you to create a view or synonym in one database referencing tables in another (only works if both databases support such links and are compatible).

Steps:

• Create database links and views in your base RDBMS.

• Connect your Anaytics Solution only to the base RDBMS.

Pros:

• No external system needed.

• Native SQL joins

Cons:

• Limited by RDBMS compatibilities; not universal.

• Cross-database links can be brittle and risky for production-scale use.

Beyond traditional data integration approaches, at chitr.io we have pioneered push-based analytics as an alternative to the conventional pull-based methods used by platforms like Tableau, Zoho Analytics, and Power BI.

Push-based analytics proactively deliver fresh insights immediately as new data becomes available, making them ideal for real-time monitoring and decision-making. Additionally, push-based analytics offer enhanced security because data is sent out actively from trusted sources rather than opening systems to inbound requests, reducing potential attack surfaces.

Stay tuned for more insights from us as we explore the power and benefits of push-based analytics in upcoming articles.

If you’re seeking advanced Analytics or Data Integration solutions, our team at chitr.io is here to help. Reach out to us anytime at info@rudracoresoft.com to discuss your specific requirements and discover how we can support your organization’s goals

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