There have been tremendous shifts in the business technology landscape in the past few years. We live in a generation where IT and business have easy access to interact because of innovation in cloud technology and mobile applications. The significant growing technologies are business technology and associated concepts such as big data in all this process. Combining these technologies and methods has unveiled the possibility of tackling new use cases that have never been considered before.
Both Business Intelligence and Big Data can be considered as two batsmen on the same pitch aiming to win for the team; however, personal scores matter. The primary purpose is to provide well-planned strategies by analyzing data that has been mined.
Thus, to help you understand the various business data processes, it is crucial to know the difference between business intelligence and data mining. So, let's take a look.
Business Intelligence incorporates data analysis with the intent of uncovering trends, insights, and patterns. It leverages data, whether big or average and extracts useful information from it. Business Intelligence solutions involve an assortment of tools, methodologies, and applications that enable data collection from internal systems and external sources, run queries against data, and organize it for analysis. It also creates reports and visualizations to present the results in an easy-to-understand way.
It works beyond standard matrices such as financial measures, overall company satisfaction, media reach, conversions, and numerous other factors. In-depth business intelligence discloses the impact of existing practices on employee performance.
BI can see the real, quantifiable results of policy and the impact on the future of business is a powerful decision-making tool.
Big Data can be defined as large data sets that outgrow simple databases and data handling architectures. For instance, data that can't be quickly taken in an excel spreadsheet may be referred to as Big Data. It comprises the process of processing, storing, and visualizing data.
Setting up an effective Big Data environment includes utilizing infrastructural technologies that process, store, and facilitate data analysis. Some of the examples are data warehouses, OLAP cubes, and modeling languages.
Nowadays, businesses often use more than one infrastructural deployment to handle multiple aspects of their data. It is crucial to find the right tools to successfully create the best environment to attain valuable insights from your data.
Thus, there is inherent usefulness to the information being collected in big data. Businesses must set relevant objectives and parameters in place to glean valuable insight from big data.
The Difference between Business Intelligence (BI) and Big Data
Business intelligence needs an operating system, ERP databases, dashboards, data warehouse components. In comparison, Big Data requires spark, hive, R servers, Hadoop, HDFS, etc.
Business intelligence allows users to make effective decisions and provide accurate reports by retrieving information from the primary data.
On the other hand, the critical purpose of Big Data is to record data, operate it, analyze data for both unstructured and structured to boost customer output.
Business Intelligence offers the following benefits, such as:
- Generating precise and quick reports
- Boosts work procedure
- Feature the standard output
- Helps in making decisions
- Increase returns
Big Data is all about data; however, it is also about a set of technologies that have been created or old technologies evolving to allow a deal with Big Data.
These technologies depend on computer clusters that leverage distributed storage and disturbed computing. In Business Intelligence, it is also relevant to talk about how traditional databases have evolved to tackle Big Data.
The Hadoop Ecosystem
There are several; terms related to the Hadoop ecosystem, such as Data Factory, Big Data Warehouse, Data Lake, Data Reservoir, etc. Some of these terms are more famous than others. Before it seemed Hadoop and related terms were competitors of traditional Business Intelligence architecture; the current trend is understanding and treating them as complementary.
Hadoop is a computer cluster that allows dealing with:
Any data (Variety), structured data like databases and CSV/Excel files, however, is unstructured or semi-structured, such as web and click logs, sensor data, social media, and more.
Any amount of data (Volume), if horse-power is assigned to the task. Hadoop is not magic; if you have lots of data, you will require many computer systems to deal with it; Hadoop makes this process easier. Cloud is the seamless partner for Big Data since it provides instant access to infinite computer resources and enables upfront scaling up and down systems.
Any time constraints regarding processing, ingestion, and analysis (Velocity): Data can be implanted in batch mode with some periodicity, usually regular, as in traditional Business Intelligence. However, also in real-time, i.e., when generated. Similarly, data can be processed and ready for analytic consumption in the meantime.
These points show that Business Intelligence Services are no more limited to structured data updated once a day, with many data leading to poor performance. Business Intelligence opens up a world of possibilities; we can now use and analyze any data of whatever size or speed we need.
Now the question comes how Hadoop integrates into Business Intelligence architecture? Let's look into this.
Hadoop can be used to change both ELT/ETL tools and DWH RDBMS technologies; the most common tactic is still to use an RDBMS as a DWH and Hadoop as an engine that prepares the Big data for it. Few reasons for keeping RDBMS as a DWH are:
- RDBMSs are mature
- The considerable investment has been made in the existing DWH
- The SQL language in RDBMS enables more features and easy to use
- Traditional RDBMS technologies are robust security-wise
Widespread use of Hadoop is Data Lake, the repository where a company stores all its data, both processed and raw.
Hadoop can be used as an enabler for Advanced Analytics use cases. Machine Learning processes can be time-consuming when done over large data sets, as in most cases, large data sets are needed. It also offers engines that allow running Machine Learning and Data Science methods on Big Data.
In some cases, Hadoop ecosystem tools like Spark Streaming, Kafka, Flume, etc., are used to generate real-time Big Data ingestion and processing pipelines that offer immediate data analysis.
Development in Database Technologies
As we know, Hadoop is not only technology that helps to evolve Business Intelligence, but traditional BI architectures rely on single-server row-based RDBMSs as the technologies used by their DWH. These work well with a small amount of data, but these technologies suffer from more extensive data sets.
Columnar Storage formats offer much better performance for analytical queries than their row-based counterparts. For instance, when computing the average age of a customer base, access to data is much more efficient if all ages are stored together.
Compressed formats use advanced compressions algorithms that leverage, among other optimizations, columnar formats to reduce IO operations.
In-memory processing leverages and optimized memory use to decrease the latency of queries using a smart caching mechanism.
Massive Parallel Processing (MPP) engines leverage various core machines to offer outstanding performances.
BI With Big Data
We have seen all the uses of Hadoop in BI architecture and upgraded our DWH database technology to leverage all our optimizations. Now, there are some experts like Cubix to help you with your Big Data Projects.They can analyze your BI architecture to find if you require to upgrade to BI with Big Data.
It helps you to define your Big Data strategy and determine Big Data use cases. Experts implement Big Data Services and Solutions, such as BI with Big Data architectures tuned for your organization.
Establish the potential of Big Data in your company via Big Data PoCs.The dive into the Big Data landscape and work together to determine the company's most suitable technology matrix.