Whitepaper: Large File Data Ingestion

Future-proof big data integration against
the mind-boggling “4Vs"

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Whitepaper - Large File -Multi-GB Data

Multi-GB Data and Industry Challenges

Currently, enterprises are sitting at the centre of a ‘data storm’. Exorbitant volume, variety, velocity, and velocity of data affects enterprises in the form of performance lags and downtimes. To handle such ginormous, highly complex data, IT squads employ robust managing systems that include hardware and software; it calls for money, time, and workforce.

To conclude, the 4 Vs of data introduces a multitude of problems, here are a few:

Heavy Computational Needs to Process Data: Today’s enterprise systems are high-volume, high-variety, and high-velocity information reservoirs. They generate data in several forms: including Schema-free JSON, Relational and NoSQL data, non-flat data (Avro, Parquet, and XML), etc.

Processing this data is difficult as enterprises require large amount of processing capacity and memory. Multi-GB data processing in real-time requires continual data input and output process. The data should be processed in a small time period. Enterprises are required to invest heavily in buying large servers and storage systems or increase capacity in hardware and bandwidth offered by hosting provider or data centers.

System Downtimes and Errors: A large database that includes a hardware & software stack grows along with upgrades, buffer, volume, log files, caches, buffers, etc. Data warehouses become a dynamic environment which becomes difficult to manage. Lack of data processing capacity & memory crashes applications or servers lead to many errors. Here are some challenges associated with the environment:

  • The database is not well normalized, it requires frequent rekeying to eliminate inconsistencies, redundancies and improve response time.
  • Application’s data access layer is not programmed efficiently and leads to slow queries.
  • Server hardware failures cause the database server to stop abruptly.
  • File permission issues corrupt data, and index files 

Need for Manual Oversight and Monitoring: Monitoring large data sets is important for low-latency processing but it is quite complex. Teams face several challenges while analyzing, curating, visualizing, and storing data. Without monitoring mechanisms it becomes difficult to manage larger and faster data sets. SQL-based datasets don’t address this problem as they can’t be scaled as per growth in monitoring information. Binary elements provide limited flow query options.

Enterprises don’t get a clear view of events occurring on servers and they fail to detect triggers that are created by different processes. Many processes run late and they go unnoticed. Enterprises miss out on compliances & service level agreement because of lack of ability to monitor files, directories, processes, underrun, errors, etc.

Data Losses and Errors: There are a number of reasons because of which data losses occur during large file data processing.Here are some ways by which enterprises lose data:

  • Overflow Errors. These errors occur when the result from any calculation is too large to be stored in a memory space. For instance, an overflow error can occur if a 9-bit number is calculated for a byte with 8 bits.  
  • Truncation Errors.  A truncation error occurs when a system truncates a long fractional part to adjust it in the allocated memory.
  • Transcription Errors. These errors are also called as misreading or transposition errors. These errors occur because of incorrect reading of the source by a user. Incorrect arrangement of characters, or lowering a digit can cause these errors.
  • Algorithm Errors. Wrong usage of algorithms or procedural steps can lead to data losses.

Complex and Cumbersome Approaches: Manual processing of multi-GB data is another world as compared to event-based continuous, seamless, automated processing. It involves batch processing, where data is collected and processed in small batches and then merged into a meaningful output. Many of these processes are from the days of mainframes and punch cards. These methods to process large data sets are inherently manual, slow, cumbersome, and error-prone.

It can be a struggle to mine enterprise data, stream workloads, and address data sharing requirements using this approach. Each stack must be updated several times for adjusting them to new data sharing requirements. Moreover, enterprises don’t get multi-tenant capabilities to facilitate customizations for optimizing digital initiatives. In this way, conventional data processing becomes a traditional cost centre instead of a business enabler. 

4Vs of big data are “BIG” integration hurdles    Know More

Whitepaper - Large File -Handling Data Exchange

Current Way of Handling Large File Data Exchange

Enterprises use different approaches for navigating large data sets, which don’t necessarily conform to expectations of quality. Many of these approaches don’t promise guaranteed smooth processing of data residing in mature and functional warehouses. These methods can be complicated, they defeat the objective and just recreates silos.

Dedicated Hardware Appliances to Accelerate Large File Processing. Enterprises utilize specialized hardware appliance equipment, such as IBM Data Power, to parse and process large data files. These appliances require heavy installation setup and specialized hardware, which needs to be maintained and updated from time to time to meet the ever-changing data dynamics in business. Apart from its rigid and unexpansive nature, appliances are difficult to operate and calls for an expert intervention. 

Custom Coding. Most enterprises use the approach of writing specialized custom coding for processing multi-GB data. High level of expertise and skill set is required to write custom codes that split large files into smaller chunks and then merge the obtained results. This coding snowballs and becomes difficult to maintain and upgrade. Rigid coding fails to scale with new coding requirements and data files. It also requires manual oversight to ensure correct processing and handling of frequent errors and exceptions. 

Big Data Software Tools. Big Data tools are often nowadays used for collection of data sets but this form of data processing has major limitations. The purpose of this data collection is mostly for analytical purposes. Another relevant drawback associated with Big Data is that it requires heavy coding and expertise for moving data files and is just an extension of custom coding approach. Enterprises need training and expertise to use Big Data tools properly. New code needs to be written for processing new data files and big data tools typically only work with simpler, flat data formats like log files and not complex or hierarchical multi-record formats.

Whitepaper - Large File -Fixes this Critical Problem

Adeptia’s Large File Data Ingestion Solution Fixes this Critical Problem

Large File Data Ingestion Solution provides key breakthroughs as compared to traditional solutions and costly appliances for multi-GB data processing. This feature can process multi-GB files, ingest and transform large colossal amount of data, and deliver this data in designated common formats.

Large File Data Ingestion Solution Processes using typical server hardware:

  • 25GB XML file with complex transformation rules in 33 minutes.
  • 200GB XML with complex transformation rules in 4 hours.
  • 50 different XML files of 25GB concurrently with complex transformation rules in 10 hours.
  • 10 different text files of 5GB concurrently in less than an hour.

 Powerful features for end-to-end data management without expensive hardware and separate infrastructure:

  • Data Quality Management: Enables enterprises to process, consolidate, and maintain a lineage of data. Ensures that enterprise data meets precise standards, compliances, and business rules.
  • Business Data Lake Architecture: Makes data ready for big data projects & applications and aligns it with organizations business objectives. Hub & Spoke Model supports Big Data projects: real-time analytics, Data Lake, etc.
  • Metadata Management: Meta-driven architecture manages both structured and unstructured data in EDI files or non-EDI files. It supports any-to-any data transformation and workflows for handling exceptions.  
  • Data Monitoring: Rich monitoring and tracking interface with real-time dashboards helps operational users to track and get all the data without the need of going to multiple systems. 
  • Data Protection: Industry-leading secure communications framework ensures full data security with encryption, guaranteed data delivery, VAN connectivity, etc. essential for high volume file transferring. 
  • Enterprise Data Governance: Flexible data governance strategy helps in shaping business data in new & unique ways. Enables enterprises to manage data lifecycle for compliances and other purposes.  

CTA - large File Data Ingestion 1 - Whitepaper

Handing large file data exchange becomes a piece of cake with Adeptia   Know More

Whitepaper - Large File -Data Handling Approaches

Large File Data Ingestion & Streaming Vs  Current Generation Data Handling Approaches

A unique feature about Large File Data Processing approach is that it is offered as a pure-software solution vs. the old approach of hardware appliance or custom-coded solution. Its easy-to-manage setup helps clients get up & running with high speed data processing over slower and manual old approach. The solution ensures architectural coherence, centralized management, security, automated error handling and, top-down control interface to reduce large file data processing time.

  • Allows non-technical business users to process large files easily without manually coding or relying on specialized IT staff.
  • Reduces manual effort and cost overheads, ultimately accelerating delivery time.
  • Eliminates requirement of expensive hardware, IT databases, and servers
  • Handles large data volumes and velocity by easily processing up to 100GB or larger files
  • Handles data variety by supporting structured data in various formats, ranging from Text/CSV flat files to complex, hierarchical XML and fixed-length formats
  • Handles data veracity by allowing data validation, cleansing and data integrity rules to be applied while processing the data

Whitepaper - Large File -Understanding Large File

Whitepaper - Large File -Understanding Large File

Understanding Large File Data Ingestion & Streaming in Detail

Large File Data Ingestion and streaming capability is built from the ground up to address the common big data problems. Business users get constructs to stream multi-GB data in heterogeneous formats so that it is accessible in a consistent manner. The solution offers business user capabilities to enable data streaming at speeds the future demands:   

  • Transforms large data sets easily, quickly and economically.
  • Minimizes latency (delay) and downtimes: Business users can see results before they read the whole input.
  • Hands data processing to business users: Minimizes resources when handling many small documents.
  • Handles real-time data feeds: Streaming can be scaled for data that is infinite, that never stops. 

Large File Data Ingestion capability provides streaming ability to process large Volume, Variety, Velocity, and Variety of data sets without the need of building a tree in memory. It provides a range of constructs for processing large sets of documents in streaming mode.

At its heart resides a streaming mode for processing gargantuan data sets. The data which needs to be processed is read by a powerful transform engine which eliminates the need of building a tree representation of the document in memory. Streaming allows the engine to avoid rewriting streamable constructs into a non-streamable form and to start producing output before it has finished receiving its full input, thus reducing latency.

The data life cycle, from ingestion, manipulation can be tracked whenever there is a change. The platform allows people, processes and technologies to consume data and push data out to other sources, cloud stores or connected devices. Enterprises can get smart about their data and process it towards the stream of business opportunities.

The Large File Data Ingestion solution allows large file sizes of 100s of GBs running on a single server (with say 4 cores and 16 GB RAM) to be ingested, processed and transformed in a short amount of time without the need of correspondingly large amounts of memory.

To illustrate: Suppose there is a very flat XML document (no deep nesting) such as an employee file with data of 100,000 employees in it. There are 100,000 elements at level 2 in the tree. Large File Data Ingestion solution processes each employee in turn. It builds a tree for each employee and processes that tree in normal (non-streaming) way. It never builds a tree for the entire file, so the memory requirements are bounded to just the amount of space needed to hold a single employee element. This sort of streaming has got practical potential to stream 10s and 100s of GB of data.

Whitepaper - Large File -Business Benefits

Business Benefits of Large File Data Ingestion & Streaming

Large File Data Ingestion is built to meet today’s scalability, efficiency, and zero-impact requirements of data processing. It is an enhanced functionality for processing and moving huge sources of real-time data to target systems or various users. Enterprises of all sizes can use this functionality for monetizing data and supporting enterprise initiatives. With this capability, the data processing power of an enterprise increases by 80X and they don’t have to stop for processing data in batches. They can integrate & process data faster and meet analytics objectives without costly hardware appliances & infrastructure. 

For enterprises dealing with the 4 Vs of data, the next round opportunity is coalescing around the large file data ingestion capability. This unique capability helps in scaling out for higher iterative data intensive work and feeding structured & unstructured data to data lakes. Data lake processing becomes more manageable, available, cost-effective, and persistent. Enterprises can synchronize cloud repositories & data warehouses and reduce batch processing loads.

Improve customer data onboarding with our Adeptia's Large file data ingestion and streaming, request a demo now. 

Whitepaper - Large File -Success Stories

Success Stories

A US National Institutes of Health (NIH) Backed Medical Research Agency

Large File Data Ingestion & Streaming capability enabled US National Institutes of Health (NIH) in achieving cost-effective and centralized ways to move very large volumes of patient research data in a controlled, secure manner by empowering data scientists and business users to drive this process. Adeptia enabled the organization in start-to-end master data management, data quality and data integration.

Financial Services Trade Association

A leading Financial Services Trade Association was providing financial products and security to over 3800 Credit Unions across the USA. The organization used Large File Data Ingestion & Streaming capability to setup a world class Partner Data Exchange solution that supported secure processing of large amounts of data to provide business intelligence and analytical services.

Whitepaper - Large File -Summary

Summary

The problem of data growth is being compounded by cutting edge technologies like big data, Internet of Things (IoT), etc. that are sources of large volume, velocity and variety of data. Leveraging and taking advantage of this data is a major challenge for all enterprises. Large File Data Ingestion capability beats this challenge by enabling multi-GB data processing across disparate sources or legacy architectures and blending it with enterprise data. This solution is an industry-leading architecture that bundles middleware processing capabilities to move and humanize data. By harnessing the speed, business users can get access to large data sets in the fastest time possible and significantly shorten the time-to-analytics. 

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