With the explosive growth of data of various data types, complexity grows even bigger with organizations amassing an estimated 80% of data in unstructured and semi-structured format. Investment goes into building data engineering teams that will be responsible for building data pipelines to efficiently and reliably deliver data.

There are though challenges when building and maintaining such complex data pipelines:

• Data engineering teams need to spend immense time hand-coding repetitive data ingestion tasks in order to move data into a data lake
• Data engineers spend time building and maintaining, and then rebuilding, complex scalable infrastructure in order to keep up with data platforms that continuously change.
• Real-time data is increasingly important. For that, low-latency data pipelines are needed, which are even more difficult to build and maintain.
• In the end, data engineers also need to focus on performance, tuning pipelines and architectures to meet SLAs

Key differentiators for successful data engineering with ConnectIQ

ConnectIQ is an API-first, cloud-native platform, federated data platform that enables data engineers have access to an end-to-end data engineering solution for ingesting, transforming, processing, scheduling and delivering data, so that they can focus on quality and reliability to drive valuable insights.

The platform’s key differentiating capabilities include:

AI – powered data automation

ConnectIQ can generate synthetic data from scratch or by looking at your existing datasets and uses AI to build data models and automatically create synthetic data for missing data combinations for data virtualization or excess scenarios on demand at a fraction of the time and cost.

This enables to overcome privacy and security challenges and get rid of bugs faster. Using ML-augmented algorithms it also enables faster and intelligent data search, better recommendations and smarter decision-making. With its SearchIQ AI Engine, it is able to uncover insights from billions of rows of data and multiple sources and delivers them to you in seconds.

The data-centric AI architecture enables to smartsize data so that you can achieve the maximum data coverage using the least amount of data possible.

Real-time or scheduled data ingestion:

Data Engineering teams are able to instantly deliver ‘fit for purpose’ and scalable data, including:

• Process data incrementally from files or streaming sources like Kafka, DBMS and NoSQL
• Automatically detect changes for structured and unstructured data formats and process the Deltas, with no manual intervention

Data quality and data governance:

ConnectIQ focuses on understanding data from a business context through AI and ML-powered automated processes like semantic discovery and classification, enforcing data integrity and data governance. It provides accurate and secure data, available in a business readable format and instantly accessible by everyone who needs it, to support and serve the needs of the organization.

Through its data-centric architecture, it embeds trust directly into data, accelerates data delivery, facilitates secure data sharing and enables rich data insights with data integrity at the core.

Data Quality-focused, Enterprise Data Pipelines:

The platform comes with a no-code, self-service environment and an easy-to-use graphical interface for creating and managing your data pipelines end-to-end.

It supports the needs and unique requirements of data consumers (analysts, engineers etc) while promoting collaboration, reusability and extensibility of data pipelines, knowledge sharing on data and data preparation. It offers simplicity and visual transformations for fast, easy data preparation by your teams without the need to write a single line of code.

Batch and stream data processing:

Data engineers have the ability to tune data latency without the need-to-know complex stream processing or implement recovery logic.

ConnectIQ unifies data from different sources and it is able to perform real-time, event-driven data streaming, based on changes and events occurring in the core banking environment. The solution offers also scheduled data synchronization method.

Automatic data pipeline deployments and operations:

It enables easy and automatic data pipeline deployments and rollbacks to minimize downtime. Benefits include:

• Complete, parameterized and automated deployment of pipelines for data continuous delivery
• End-to-end orchestration, testing and monitoring of data pipeline deployment across on premise and all major cloud providers

Data lineage and observability:

Data Lineage is a visual representation of data flow that helps track data from its origin to its destination along with all the changes. The ability to map and verify how data has been accessed and changed is critical for data transparency and data governance.

The platform has AI-powered data lineage capabilities that can help you understand data flow relationships and also brings insights into “control” relationships, such as associations and interdependencies, and logical-to-physical models. It enables continuous monitoring of data pipeline jobs to ensure continued operation.

Data orchestration:

Simple and reliable orchestration of data processing tasks for DataOps and MLOPs pipelines end-to-end, with the highest security, scalability and flexibility enabling a ‘single source of truth’ for your processes.

ConnectIQ’s game changing features simplify the data lifecycle and management by automating and maintaining all data dependencies, leveraging built-in quality controls with monitoring and providing deep visibility into pipeline operations with automatic recovery. Data engineers are now able to focus on easily and rapidly building reliable end-to-end production-ready data pipelines for all use cases.