Big Data Integration: Challenges, Techniques, Best Practices

Big Data Integration: Challenges, Techniques, Best Practices

Did you know each person living on this earth today who is using digital equipment can easily produce over 1.7MB of data every second of the day? Experts predict that this number will continue to soar to new heights as time goes on. As this number rises, the likelihood of traditional data integration techniques being able to handle the load plummets. Big Data has rendered old ways of integration obsolete and ushered in new methods of managing its volume, variety, velocity, and veracity. Let us find out how we can sort them with a series of techniques and Big Data integration best practices to simplify your path to digital transformation. 

The 4 V’s of Big Data 

Data integration is the process of combining data from multiple separate business systems into a single unified view to produce actionable business intelligence. However, with the 4 V’s of Big Data—volume, variety, velocity, and veracity—integration can become tricky unless appropriate strategies and techniques are used. 

Volume: Large data volumes require extensive resources that you must reserve for harvesting, processing, and storing datasets. This can come at a huge price unless an extensive computing network like Hadoop is utilized. Skilled human resources that understand the logistics of streaming Big Data would be required.  

Variety: A large dataset adds no value until it is in an understandable form.  Datasets that come from multiple sources are unique and might follow different schemas. To make sense of such datasets, you need to have sophisticated knowledge and advanced analytical capacities.   

Velocity: Data is extremely time sensitive and can become obsolete within seconds. With the real time speed in demand, the pressure is on data integration. 

Veracity: To make informed decisions, enterprises must have access to accurate, and reliable data. However, a Forrester Consulting survey in 2019 had revealed that only 38% of the participating businesses had such confidence in their insights. 

Big Data Integration Techniques 

As already stated, traditional methods of data integration typically used data consolidation as the primary method. Data is consolidated from multiple resources into a single centralized repository, and this takes anything from a few seconds to a few hours or more depending on the technology used. An alternative to this method is data federation. A federated database not only consolidates data from multiple sources but also eases access for front-end application users by building separate centralized sources virtually. Some organizations also used data propagation as the primary method of integration. In this method, the data pulled from an enterprise data warehouse is transferred to data marts continuously, and if the data changes in the warehouse, the connection reflects the change in data marts. 

To perform any of the above-listed forms of data integration, the most popular techniques used today are enterprise data replication (EDR); extract, transform, load (ETL); and enterprise information integration (EII).  

EDR is a simple technique of data consolidation that sporadically picks data from one storage system to transfer to another. This is not the best choice for Big Data integration though, because it cannot handle the pressure of the real-time flow of large volumes of information.  

In ETL, one of the most popular methods, the data is extracted from a source, transformed, then transferred to the destination database. ETL traditionally follows a batch process but with Big Data systems, data loading can happen in real time using change data capture (CDC). However, the ETL approach is not fully attuned to Big Data needs and thus, is susceptible to the 4 V’s challenges. Some of the challenges faced with ETL tools are discussed here. 

To remedy this, traditional ETL systems have been modified at the levels of schema mapping, record linkage, and data fusion to make them more suitable for Big Data integration. Let us dig into each of these to understand how they work.  

Schema Mapping: The traditional data integration method needs schema mapping to be exacted at the source which requires significant efforts. But by allowing mapping between local and global schemas at later stages, data integration can be simplified. This approach can address the challenges of volume and variety along the path of gradual evolution. 

Please note that for traditional integration focused mapping on the databases for Big Data mapping, you must also map your business processes before performing integration. 

Record Linkage: In traditional data integration, record linkages were performed between homogenous data sources. But since Big Data sources are heterogenous, you can use incremental and parallel record linkages made possible by a tool like MapReduce. Incremental linkages can take care of high-speed data flows thereby fixing the velocity problem. Linking between unstructured and structured data further resolves the challenge of variety.  

Data Fusion: The web is filled with huge sets of data, but not all data contains true information. How can one deal with such conflicts and ensure that only a single truth is captured? The fusion of online data from various sources can help identify these anomalies and discover the correct data for storage. When combined with parallel record linkages, the variety of data is also taken care of.  

Popular ETL tools that can be used for Big Data Integration are IBM Infosphere Datastage, Oracle Warehouse Builder, SAS Data Integration Studio, and Informatica PowerCenter to name a few. 

EII can deliver curated datasets and build a virtual layer so that business applications do not have to deal with the complexities of data sources. EII is far better at fetching data in real time than ETL and allows business users to perform analysis on fresh data. It can reduce data storage needs, provide real time data access, and support faster development through incremental procedures. 

Popular data federation tools include SAP BusinessObjects Data Federator, Oracle Data Service Integrator, IBM InfoSphere Data Integration Server, and Sybase Data Federation.  

An Easier Alternative to ETL 

Another alternative to ETL is extract, load, transfer (ELT), which puts the work of transformation on the application layer and the raw, unstructured data is consolidated and delivered to the applications. This approach has gained much popularity because of 3rd party cloud-based tools like Spark, Hadoop, Snowflake, and Databricks that can work with unstructured data. The ETL integration technique provides several benefits including: 

  • While ELT removes the burden of transformation for integrators, it also gives freedom to the data analyst as they can investigate the data on their chosen platform 
  • It makes way for real time data integration by extracting data from different sources and delivering it to a common platform with sub-second latency 
  • With ELT integration, a multi-cloud strategy is easy to incorporate as it allows companies to choose from a broad range of Big Data analytics services instead of sticking with one vendor 
  • Data extraction and loading processes are no longer the concerns of upstream processes, and the workflow is simpler and shorter 
  • Data Engineers are free of transformation hassles and can focus on rendering projects rather than preparing data 
  • With transformation no longer included in the process, the data consolidation can be done faster and in real time 

Selecting the Right Tool for Big Data Integration 

Choosing the right Big Data integration tool is all about weighing the pros and cons against your priorities; there is no one-size-fits-all solution. You’ll need to decide between a custom API or a standard data integration platform. For the masses choosing the latter, we’ve compiled a list of popular off-the-shelf Big Data integration technologies and their benefits and downfalls.  

Integration Technology  Pros  Cons 
Amazon Dynamo 

Uses Dynamo’s Data Integration Framework (DIF) to eliminate manual uploads 

Unified asset management platform eliminates need to store data in multiple systems  Can be costly when working with multiple environments 
Google BigTable 

NoSQL cloud-based system that integrates with Apache ecosystem and other Google products 

Completely managed 

High scalability 

Tight integration with Google ecosystem 

No Native connector  

Cannot work with on-premises deployment 

Functions provided are not comprehensive 

Casandra 

NoSQL database to provide data storage and retrieval mechanisms 

High scalability 

High availability 

Replication across data centers 

High fault tolerance with easy replacement of failed nodes without furnishing downtime.  

Limited query options 

No referential integrity 

Limited predesigned functions for design decisions 

Hadoop 

Uses HDFS (Hadoop File System) for large-scale data and MapReduce for parallel processing 

Can handle large capacities and capabilities 

Cost-effective solution 

Unfit for small data streams 

No encryption at storage & network levels 

Hive 

Used for summarizing data and for ad hoc queries through HiveQL language. 

Fast, scalable, and extensive  Single point of failure 

No support for SQL structure 

No Built-in authentication 

Flume 

Distributed system that consolidates data into a centralized store 

Reliable system for aggregating large data sets  No data replication 

Complex topology makes configuration challenging 

Spark 

Uses own cluster for data storage and processing 

Interactive queries 

Stream processing 

In-memory cluster computing 

Not fit for multi-user environment 

No automatic optimization 

Kafka 

Distributed messaging system built on Zookeeper synchronization service and integrates Apache Storm and Spark 

High throughput 

Low latency 

Fault tolerant 

Real-time data handling 

Lacks some monitoring capabilities 

No support for wildcard topic selection 

MongoDB 

Document-oriented cross-platform storage that stores data in JSON format 

High availability 

Fast updates 

Rich Queries 

High memory usage 

Limits data size and nesting 

ElasticSearch 

Open-source search and analytics engine that replaces document-based storage 

Real-time distribution 

High scalability 

Denormalization improves search performance 

High complexity 

High cost 

 

Starting Your Big Data Integration Journey 

Your information is an invaluable asset you can’t afford to mishandle. Traditional ways of bringing your data into a singular and ready-to-use view just won’t cut it anymore. Big Data integration can help. Choosing the right techniques and seeking help from the right team of professionals if/when necessary, can put you on the fast track to digital transformation and data-powered business intelligence.  

To learn more about how to augment your Big Data integration, check out Apexon’s  Data Engineering services or get in touch directly using the form below. 

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