I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

Full Bio 
Follow on

I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

Why is there so much buzz around Predictive Analytics?
1024 days ago

Changing Scenario of Automation over the years
1025 days ago

Top 7 trending technologies in 2018
1026 days ago

A Beginner's Manual to Data Science & Data Analytics
1026 days ago

Artificial Intelligence: A big boon for recruitment?
1027 days ago

Top 5 chatbot platforms in India

Artificial Intelligence: Real-World Applications

How can Big Data Analytics influence Decision Making?

Why is there so much buzz around Predictive Analytics?

Levels of Big Data Maturity

Key technologies for enabling big data analytics for businesses

By shiwaneeg |Email | Apr 10, 2018 | 24777 Views

Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Their effectiveness depends on the 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

Businesses' prime goal is to avoid risks in decision making. Here, predictive analytics plays a major role. Predictive analytics hardware and software solutions can be utilized for discovery, evaluation and deployment of predictive scenarios by processing big data.

2) NoSQL Databases

NoSQL databases are utilized for reliable and efficient data management across a scalable number of storage nodes. These databases store data as relational database tables, JSON docs or key-value pairings.

3) Knowledge Discovery Tools

Knowledge Discovery Tools 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 utilize the information to their benefit.

4) Stream Analytics

Sometimes the data an organization 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

Data Preprocessing can be 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.

This article was originally published in MarutiTech Labs.

Source: Maruti Tech Labs