Large File Data Ingestion at the Speed of Business

While big data offers a ton of benefits, it comes with its own set of issues. Its four components, volume, variety, velocity and veracity, make the data ingestion process tedious and cumbersome. Both moving and processing part is not easy. Moving data like orders, invoices, point of sales data, employee data, marketing data, etc. or unstructured data (such as images, videos, scanned documents, etc.) into data lakes is clumsy.

Enterprises often try manual chunking of data into smaller data sets that need to be aggregated post-processing, but it’s not a smart path to take. It almost always requires highly skilled developers for implementing a very complex mechanism of chunking and aggregating, and even then, it is difficult, prone to errors, and remarkably inefficient. While appliances like IBM DataPower can get the job done, the approach is too expensive and too hard to maintain or upgrade.

With the dawn of big data, enterprises are looking for smarter movement of large-scale data that drives better business decisions and improves business bottom line.They need a “smart” data lake ingestion strategy that enables data analysts incorporate an automated extract, transform, load (ETL) procedure to gather data from sources, process information, and store it into a data lake without error or discrepancy. The need of the hour is deploying a big data ingestion tool that takes data from different external sources and disparate formats, combines them with internal sources and standards, and merges the large volume of data in real-time.

Our customers base is strong support for us. While talking to them, we found that most of our large scale customers, which includes giants in the insurance and finance domain, were facing a similar challenge. They found it hard to process data in multiple formats, such as XML, CSV, or PDFs, or that ranged from a few KBs to 100s of MBs to 10s of GBs per file. Plus, storing such large chunks of data was back-breaking.

They needed data ingestion software tool not only for processing large, flat, or hierarchical files but also for streaming data transformation in parallel to process the data and deposit in a data warehouse through a real-time ETL. The data needed to be cleansed of errors, validated with business rules, transformed by normalizing into a common format. The challenge was multi-fold.

When our customers ran these large multi-GB files through their existing data ingestion platforms tools, those applications immediately crashed. Our customers increased the memory and system requirements, reran the large files for processing, and the applications crashed again. Then they wrote custom scripts and programs to process these large multi-GB files, some of them were processed but most of them crashed the custom programs. These mundane efforts of data preparation and transformation were complex and difficult to operationalize.

A software solution that was free from the limitations of available data ingestion platforms was needed, and Adeptia recognized that need.

Adeptia built a power big data solution that processes multi-GB files, ingests and transforms large volume of data, and delivers that data in a common format timely and reliably. Its unique self-service powered approach to big data or large file ingestion allows users (even non-technical users) to process both flat and hierarchical files in any format — XML, CSV, Text, or PDF — and delivers to a normalized format or data warehouse — without heavy coding or architecture.

Adeptia data ingestion solution is a fully managed, simple and extendable ETL-based model for efficiently extracting, and moving large amounts of data in real-time. Our solution supports many use cases: self-service integration, real-time analytics, continuous computation, data Lake, etc. It is scalable, fault-tolerant, and easy to set up and operate.

Our large file data ingestion capability has multiple real-world applications, including how our clients are currently using this self-service functionality to drive business intelligence and informed decision-making without additional support.

A US Department of Health and Human Services backed medical research agency aggregates sensitive medical data from all around the country. The research agency interacts with medical centers, health clinics, and medical insurance providers to receive medical records in multiple formats and from multiple sources. Adeptia’s large file data ingestion software solution acts as the central receiver of this data, ingests it, transforms it while streaming it into the agency’s data warehouse for driving analytics, research and decisions. The self-service data ingestion approach makes it easier for non-technical users connect sensitive medical data from assorted sources into a destination system to boot.

A large North American credit union has connected with smaller credit unions across the country to exchange and aggregate data. The data comes from multiple source applications and databases and in multiple non-standardized formats including large multi-GB XML and CSV files. This data is ingested by Adeptia’s self-service data lake ingestion solution, streamed and transformed in parallel, and ultimately sent to the data lake at the credit union.

As a general scenario, our large file data ingestion feature helped in handling the large incoming volume of data at large enterprises (hubs) from multiple external or internal sources (spokes). This feature processes files that are multi-GB in size, accepts all formats and file sizes, including flat or hierarchical files, and ingests and streams data in parallel to deposit in a central data warehouse or data lake at the hub company. Adeptia data lake ingestion solution is proven in production environments for ingesting data feeds which are continuous or asynchronous and real-time or batched with no data loss and no human intervention

Adeptia’s data ingestion software approach for handling large multi-GB files in real-time offers many benefits over traditional solutions for large data file processing.

• The self-service ingestion approach (rather than relying on hardware appliance) allows non-technical business users to process large files easily without manually coding or relying on specialized IT staff. This frees IT to focus on the governance role instead of the operational one.
• With reduced load on specialized IT staff, Adeptia’s solution reduces manual effort and system resource costs, ultimately accelerating time-to-market.
• Hubs and large enterprises do not need to support expensive infrastructure or specialized servers for supporting appliances for large file data ingestion.

Adeptia’s unique approach of self-service parallel ingestion of large data along with runtime data transformation and streaming is a competitive edge that lets you save time, accelerate service delivery, fast-forward revenues, and ultimately become easier to do business with.