Big Data Databases: the Essence
Big data is multi-source, massive-volume data of different nature (structured, semi-structured, and unstructured) that requires a special approach to storage and processing.
The distinctive feature of big data databases is the absence of rigid schemas and the ability to store petabytes of data. NoSQL (non-relational) database systems are optimized for big data. They are built on a horizontal architecture and enable quick and cost-effective processing of large data volumes and multiple concurrent queries.
Relational databases (RDBMS)
Non-relational databases (non-RDBMS)
Data
Schema
Scalability
Language
Transaction
Best for
Examples
Structured data is stored in tables
Supports rigid (pre-defined) data schema
Vertically scalable
Structured query language
ACID-compliant (atomicity, consistency, isolation, durability)
Complex queries, database transactions and routine data analysis
Amazon Redshift, Azure Synapse Analytics, Microsoft SQL Server, Oracle Database, MySQL, IBM DB2, etc.
Unstructured data is stored according to different models (key-value, document-oriented, graph, wide-column store, and multi-model)
Supports dynamic data schema
Horizontally scalable
Unstructured query language
CAP theorem (consistency, availability and partition tolerance), may be ACID-compliant
Storing and modeling structured, semi-structured and unstructured data
Amazon DynamoDB, Azure Cosmos DB, Amazon Keyspaces, Amazon DocumentDB, Oracle NoSQL database, etc.
Even though non-relational databases have proved to be better for high-performance and agile processing of data at scale, such solutions as Amazon Redshift and Azure Synapse Analytics are now optimized for querying massive data sets, which makes them sufficient when dealing with big data.
Big Data Architecture and the Place of Big Data Databases in It
Big data architecture may include the following components:
- Data sources – relational databases, files (e.g., web server log files) produced by applications, real-time data produced by IoT devices.
- Big data storage – NoSQL databases for storing high data volumes of different types before filtering, aggregating and preparing data for analysis.
- Real-time message ingestion store – to capture and store real-time messages for stream processing.
- Analytical data store – relational databases for preparing and structuring big data for further analytical querying.
- Big data analytics and reporting, which may include OLAP cubes, ML tools, self-service BI tools, etc. – to provide big data insights to end users.
Features of Big Data Databases
Best Big Data Databases for Comparison
According to the Forrester Wave report, some of the best databases for data analytics and processing are Amazon DynamoDB, Azure Cosmos DB, and MongoDB. Having proven expertise in market-leading techs, ScienceSoft is a technology-neutral vendor, and our choice of the optimal toolset is based on the value it will bring in each case.
Below, our experts provide a comparison of several big data databases ScienceSoft uses in its projects.
What Big Data Database Suits Your Needs?
There is no one-size-fits-all big data database. Please share your data nature, database usage, performance, and security requirements. ScienceSoft's big data experts will recommend a database that is best for your specific case.
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Thank you for your request!
We will analyze your case and get back to you within a business day to share a ballpark estimate.
In the meantime, would you like to learn more about ScienceSoft?
- Project success no matter what: learn how we make good on our mission.
- 35 years in data management and analytics: check what we do.
- 4,000 successful projects: explore our portfolio.
- 1,300+ incredible clients: read what they say.
Big Data Database Implementation by ScienceSoft
With mature project management practicesthat we've polished for 35 years, we drive projects to their goals regardless of arising challenges, be theyrelated to time and budget constraints or changing requirements.
ScienceSoft as a Big Data Consulting Partner
ScienceSoft's team proved their mastery in a vast range of big data technologies we required: Hadoop Distributed File System, Hadoop MapReduce, Apache Hive, Apache Ambari, Apache Oozie, Apache Spark, Apache ZooKeeper are just a couple of names.
ScienceSoft's team also showed themselves great consultants. Special thanks for supporting us during the transition period. Whenever a question arose, we got it answered almost instantly.
Kaiyang Liang Ph.D.,Professor, Miami Dade College
What makes ScienceSoft different
We achieve project success no matter what
ScienceSoft does not pass off mere project administration for project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.
See how we do that
