Go to ScienceSoft Global
A Big Data Warehouse – a Want or a Need?
Editor’s note: Is ‘a big data warehouse’ just another buzzword for you? Read on to discover the role of the big data warehouse in a big data solution and have a look at ScienceSoft’s offer in big data services to learn how we help our clients leverage big data potential.
ScienceSoft’s experts in DWH services refer to the term ‘big data warehouse’ in their everyday practice. In the article, I’ll explain what they mean by the big data warehouse and how it is different from the traditional (enterprise) DWH.
Big data warehouse vs. traditional DWH
The big data warehouse is a central storage component of the big data solution’s architecture, and the difference with the traditional DWH lies in:
Data type
The traditional DWH stores homogeneous data only: records from CRM, ERP, etc. The big data warehouse is a universal storage repository: it stores both traditional data and heterogeneous big data – transactional data, sensor data, weblogs, audio, video, official statistics, and others.
Data volume
Enterprise data warehouses cannot deal with a very large volume of data (typically, they store terabytes of data). As for big data warehouses, they allow storing petabytes of data and beyond. Surely, such volumes need proper management, and here we share our experience on how the properly chosen technology stack can tackle this task for our clients.
Approach to data quality
The traditional DWH demands data to be consistent, accurate, complete, auditable, and orderly.
When speaking of big data quality, it is impossible to meet the above requirements, and, luckily, there is no need to. Data experts set minimal satisfactory thresholds to refine data in the big data warehouse to the ‘good-enough’ state. These thresholds vary depending on a particular task. Let’s take requirements for big data completeness, for example. When analyzing shopping trends in social media, the 100%-data completeness is not really needed – we can define customer sentiment during the autumn season without the two-day amount of data. However, in case of IoT analytics in oil and gas, – the minimal satisfactory thresholds will be higher, as without the two-day amount of data you can miss some important patterns, which can result in machinery breakdowns or oil spillages.
Technology stack
Among the technologies utilized in the traditional DWH are Microsoft SQL Server, Microsoft SSIS, Oracle, Talend, Informatica, etc.
The big data warehouse employs specific technologies that can deal with storing huge volumes, close-to-instant streaming and parallel processing of big data: HDFS, Apache Cassandra, HBase, Amazon RedShift, Apache Spark, Hadoop MapReduce, Apache Kafka, etc.
Insights
The big data warehouse architecture allows advanced AI-based analytical technologies like machine learning. By analyzing big data from multiple sources, companies can have deeper insights on enhancing business processes, make accurate predictions and generate prescriptions.
The enterprise data warehouse also employs analytics, but due to the limited amount of stored data, the above-mentioned advanced technologies, which are very data-hungry, cannot be embraced to the fullest. Thus, the analytics results only describe what happened and diagnose the reason for the outcome.
Data access
Although both DWH types pursue the common goal – delivering intelligence to decision-makers, the big data warehouse goes further as it allows rapid reporting to be available across the organization. That way, the insights are granted to a larger number of decision-makers.
It’s time to go big data
A big data solution can’t go without a big data warehouse. What is more, you may need to have it augmented with a data lake. However, if you don’t feel like diving into technical details on the way to your big data solution that addresses your business objectives, you are welcome to ask ScienceSoft’s team for a customized solution.