Elastic Cloud Data Integration: Why It’s Important

Last Published: Apr 12, 2023 |
Sudipta Datta
Sudipta Datta

Product Marketing Manager

The highs and lows of your cloud data integration workload can throttle your system and unnecessarily drain your money if you don’t implement a flexible framework.  Elasticity and resiliency of your data integration architecture not only optimize performance but also give you more granular control on cost. 


Elastic Cloud Data Integration: What Is It?

Data integration is how you connect data from disparate sources to attain a business objective. As you create a data pipeline you get to define how to process the data and at what frequency.  Based on the data volume, processing power, and frequency you allocate resources.  IT professionals have come a long way from provisioning infrastructure and resources on a yearly basis to auto allocation based on demand and AI recommendation. The core concept of elastic cloud data integration is to build a system that can handle the unpredictable data workload automatically without breaking, scaling in and scaling out on demand to help you control cost and time with its data-driven recommendations. 


Why Do You Need Elastic Cloud Data Integration? 

The digital economy needs well connected and flexible data integration architectures. Gone are the days where you rely on annual capacity planning. On the one hand, you risk running out of capacity earlier than expected, and on the other, your ROI might erode due to underutilization of your infrastructure. Here are a few factors that may trigger a decision to go for elastic cloud data integration: 


Unpredictable data load:  While it’s a given that cumulatively your organization will produce more data than last year, it’s hard to predict seasonality and regionality of that data growth. Your sandbox sessions might not provide the right picture, and it’s only in production you realize the real volume of data that’s going to hit the system. Your cloud data integration solution should be able to grow and adapt to any changes in terms of data volume, type, and sources. 


Unmanageable operational workload: The repetitive tactical work has a huge share in your teams’ to-do list barring them from focussing on the strategic work. The data integrators are spending their time provisioning on-premises and cloud infrastructure for new integration requests and troubleshooting. As the allocated budget is not proportional to the growing workload, they are looking for ways to optimize the existing data pipeline manually. Oftentimes, they resort to hand coding and open-source applications that are hard to upgrade and vulnerable to security threats. 


Poor ROIWhen you work on a new integration use case, you start by over-provisioning as you expect a growth in integration job loads. Unless you constantly monitor and adjust, you tend to lose money due to under-utilization. On the other hand, if the workload crosses a threshold capacity, the high availability of your systems is at risk and might disrupt your services. 


System breakdownWith a rigid infrastructure, the efficiency of your systems might suffer or worse it might break down with excessive data integration jobs. The consequences are huge.  Your projects will get delayed, SLAs will suffer, and you might end up disappointing your customers. 


Key Benefits of Elastic Cloud Data Integration 

Scale: Elastic cloud data integration provides a flexible framework for leveraging the tremendous amount of data generated and getting timely value from it. With auto provisioning, you can automatically scale up and scale out, depending on workload size.  


Standardize and Automate: When your elastic cloud data integration system learns the configuration details of your data pipeline from historic values and starts making recommendations, it gives you an opportunity to set a standard for your data integration jobs. Once you set the standard you can templatize and use it for repetitive patterns. You can automate the whole process, leaving the rules of adjustments to the tool. 


Optimize Performance: With elastic cloud data integration you have more control over time. You can automatically adjust horsepower needed to process big data. With massive parallel processing, you can achieve your deadlines irrespective of data volume.  


Control Cost : With on-demand data processing you only pay when your elastic cloud data integration is in use. You don’t need to pre-allocate resources and pay for idle time.  


Support Changing Data Integration Patterns: Elastic cloud data integration does not mean just accommodating any data volume, but it should be flexible enough to support different data integration patterns: from ETL to ELT and from data warehousing to data fabric


How to Make Your Cloud Data Integration More Elastic

There are three major components that can make your cloud data integration scalable. 


Auto allocation of resources: Your infrastructure plays a bigger role here. How flexible your underlying infrastructure is determines how best you can handle fluctuating workloads. Cloud is the first step that offers on-demand instances. You start on a small scale  and then can spin off VMs for your incremental data processing. With containers like Docker you can package up your processing engine with all its parts and ship it to the cloud of your choice. That helps in a multi-cloud environment and doesn’t lock you into one single cloud vendor. But you still cannot cut down on the cost that’s charged for idle time of your instances. Depending on what level you want to offload the responsibility for managing or provisioning the resources you pick cloud, VMs, containers. For zero infrastructure management you pick serverless deployment. Serverless helps you focus on building your data pipelines and applications without worrying about managing the infrastructure.


Massive parallel processingWhen dealing with large scale data you need a framework that can support parallel execution.  A system that is logically centralized yet physically distributed or partitioned is preferred to process high volumes of data with minimal disruption. The input data is split into small chunks and processed parallelly and separately. With big data we saw Hadoop gaining momentum as it enabled parallel processing. Unstructured data is processed using Hadoop Map Reduce. Then we had Spark with faster processing power and low latency. The storage and processing capacity in cluster servers determine how efficiently the data will be processed, both in terms of cost and time. 


AI-based process iteration: Infuse AI and machine learning to automatically maneuver through the changes as you move data from source to target. Instances like source schema changing or metadata getting updated have the potential to break the system. With intelligent automation you can dynamically rearrange processes to address these type of changes.  With unassisted support you not only avoid disruptions but only increase your agility and the performance of your data pipeline. You can take advantage of machine learning that can analyze log patterns and help you resolve issues faster.


How Informatica Supports Elastic Cloud Data Integration

The Informatica Cloud Data Integration Elastic service helps you build and maintain high-performance data pipelines at scale. It enables your IT team to use elastic clusters for high-volume data processing. These compute clusters are maintained on your behalf by a secure agent and can dynamically scale up or down depending on your workload. 


For design time, Cloud Data Integration Elastic provides: 


Wizards for unifying hierarchical and relational data: In the era of big data, it’s essential to sync structure, unstructured, and semi-structured, and semi-structured data types to generate coherent information. Hierarchical-to-relational  (H2R), hierarchical-to-hierarchical (H2H), and relational-to-hierarchical (R2H) transformation wizards help you convert different data types to standardized output with ease. Inclusion of different data types and functions builds the versatility and elasticity of your data integration environment.  


Incremental file loader and change data capture (CDC): To handle the velocity of new information, you need seamless, continuous integration of data without losing n efficiency and time. Features like CDC and incremental file loader help to process only the new data that pours in and update the target applications accordingly. Incremental File Load is a feature of Cloud Data Integration Elastic that processes only new files and prevents reprocessing of old data.​


Zero-code integration designer: Build and run data mappings on the go with a no-code / low-code interface. The visual mapping designer and parameterization help to reuse data artifacts and scale faster. 


Dynamic mapping: Use dynamic mapping to manage frequent schema or metadata changes or to reuse the mapping logic for data sources with different schemas.


For runtime, Cloud Data Integration Elastic provides: 


Spark processing on Kubernetes clusters: Cloud Data Integration Elastic uses Spark for large-scale data processing and Kubernetes as the orchestrator. With Spark you can process high volumes of data with high concurrency. With Cloud Data Integration Elastic you don’t have to manage Spark. Spark engines are shipped in containers managed by Kubernetes to the cloud of your choice. This helps run your data integration jobs in multiple clouds. Advanced transformation capabilities can run natively on Spark, ensuring high-speed data processing. 


Serverless data integrationServerless deployment gives you freedom from allocating and configuring infrastructure and lets you focus on planning and creating data pipelines. The execution of your data integration jobs gets streamlined and cost effective as you only pay for the time you use the function and nothing when the system is idle. 


For operations, Cloud Data Integration Elastic provides:


Auto scaling and auto tuning: Cloud Data Integration Elastic increases productivity with Informatica CLAIRE-powered auto scaling the clusters and auto-tuning of Spark parameters. Optimize performance of the Spark engine by analyzing the run-time environment and iterate based on recommendations. 


Dynamic data partitioning: Cloud Data Integration Elastic supports dynamic data partitioning. You configure the partition information, and based on the data load it automatically decides on the number of partitions needed to meet the SLAs. You have the ability  to stick to session run time limits even if the data load fluctuates. 


Cloud Data Integration Elastic empowers you to run any amount of data processing jobs in a fully managed environment and provides the scalability and flexibility you need. 


Learn more: Watch this short use case and demo video on Cloud Data Integration Elastic.

First Published: Oct 08, 2021