In today’s times, data has emerged to be the new oil. The more you understand your data, the better business decisions you are able to make. As the name suggests, big data analytics refers to managing and analysing large data sets. Big data analytics helps in extracting insights and uncovering patterns from large pools of complex data.
Not only large corporations, small and medium enterprises are also leveraging big data analytics to obtain the best possible outcomes for their businesses. Let us explore how small businesses can benefit from data analytics and learn about the technologies that enable big data analytics for businesses.
The big data analytics technology is a combination of several techniques and processing methods. What makes them effective is their collective use by enterprises to obtain relevant results for strategic management and implementation. Here is a brief on the big data technologies used by both small enterprises and large-scale corporations.
1) Predictive Analytics
One of the prime tools for businesses to avoid risks in decision making, predictive analytics can help businesses. Predictive analytics hardware and software solutions can be utilised for discovery, evaluation and deployment of predictive scenarios by processing big data.
2) NoSQL Databases
These databases are utilised for reliable and efficient data management across a scalable number of storage nodes. NoSQL databases store data as relational database tables, JSON docs or key-value pairings.
3) Knowledge Discovery Tools
These are tools that allow businesses to mine big data (structured and unstructured) which is stored on multiple sources. These sources can be different file systems, APIs, DBMS or similar platforms. With search and knowledge discovery tools, businesses can isolate and utilise the information to their benefit.
4) Stream Analytics
Sometimes the data an organisation needs to process can be stored on multiple platforms and in multiple formats. Stream analytics software is highly useful for filtering, aggregation, and analysis of such big data. Stream analytics also allows connection to external data sources and their integration into the application flow.
5) In-memory Data Fabric
This technology helps in distribution of large quantities of data across system resources such as Dynamic RAM, Flash Storage or Solid State Storage Drives. Which in turn enables low latency access and processing of big data on the connected nodes.
6) Distributed Storage
A way to counter independent node failures and loss or corruption of big data sources, distributed file stores contain replicated data. Sometimes the data is also replicated for low latency quick access on large computer networks. These are generally non-relational databases.
7) Data Virtualization
It enables applications to retrieve data without implementing technical restrictions such as data formats, the physical location of data, etc. Used by Apache Hadoop and other distributed data stores for real-time or near real-time access to data stored on various platforms, data virtualization is one of the most used big data technologies.
8) Data Integration
A key operational challenge for most organizations handling big data is to process terabytes (or petabytes) of data in a way that can be useful for customer deliverables. Data integration tools allow businesses to streamline data across a number of big data solutions such as Amazon EMR, Apache Hive, Apache Pig, Apache Spark, Hadoop, MapReduce, MongoDB and Couchbase.
9) Data Preprocessing
These software solutions are used for manipulation of data into a format that is consistent and can be used for further analysis. The data preparation tools accelerate the data sharing process by formatting and cleansing unstructured data sets. A limitation of data preprocessing is that all its tasks cannot be automated and require human oversight, which can be tedious and time-consuming.
10) Data Quality
An important parameter for big data processing is the data quality. The data quality software can conduct cleansing and enrichment of large data sets by utilising parallel processing. These softwares are widely used for getting consistent and reliable outputs from big data processing.
