Big Data Tools and Technologies

Tools and Technologies are always in demand to meet modern enterprise’s requirements. Now, modern enterprises mostly rely on real-time, predictive, and integrated insights on data science and its life cycle. Data creation and consumption continues to grow by leaps and bounds investing in data scientists, big data analytic hardware, software and services for continuing education.

Modern technologies and tools make it possible to realize value from Big Data and its analytics. Big data is always in the big demand with changing business environment, the majority portion of IT investment goes towards managing and maintaining of Big Data. Data at any scale and in any form going mainstream for new age businesses.

Leading big data tool and technologies based on analysis by experts.

NoSQL Databases:

Document, key-value, and graph databases.

Predictive Analytics:

Predictive model deployment by analyzing big data sources to improve business performance and mitigate risk. A combined solution with big data software and hardware allow enterprises to discover, evaluate, optimize and deployment decisions.

Data Integration:

Full scale data orchestration tool such as Mongo DB, Hadoop, MapReduce, Amazon Elastic MapReduce (EMR), Apache Pig, Apache Spark, Apache Hive, Couchbase.

Cloud Data Warehouse:

Enterprise data warehouse for analytics with integrated end to end big data solution on cloud platform mainly by AWS and Google.

In-memory Data Fabric:

Distributing data across the dynamic random-access memory (DRAM), Flash, or SSD of a distributed computer system, importantly that provide low-latency access and processing of enormous quantities.

Search and Knowledge Discovery:

self-service information extraction tools and technologies to support the greater insights from large repositories for unstructured and structured data in multiple sources like file systems, databases, streams, APIs, and other platforms and applications.

Data Preparation:

Software to set up data for analytics use. Sets of software to ease sourcing, shaping, cleansing, and sharing diverse and messy data sets to accelerate data analytics.

Data Quality:

Tools and products for data cleansing, enrichment on large, high-velocity data sets, using parallel operations for distributed databases and storage.

Stream Analytics:

Sets of tools for real time data filtration, aggregation, enrich and analyze a high data volume from live data sources in any data format.

Data virtualization:

Tools that delivers real time or near to real time information from various data sources like Hadoop and distributed data stores.

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Big data ecosystem is constantly evolving and modern technologies gets better to use analytics at micro level for business intelligence. Data engineers require above listed many big data tools to create, pull, clean and filter for specific sets of large data volumes to help data scientists for further usage.  

As a conclusion, Modern tools and technologies continues to expand more rapidly than the overall market, possibly offsetting declines in traditional spending. The modern big data and analytics platform emerged in the last few years to meet enterprises requirements for accessibility, intelligence, agility and deeper analytical insight. Ultimately shifting the market from IT-led to business-led with big data usage and analytics including self-service.