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5 Best Big Data Databases

Features, Benefits, Pricing

With 11 years in big data services, ScienceSoft assists companies with selecting and implementing proper software for their big data initiatives.

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Top 5 Big Data Databases - ScienceSoft
Top 5 Big Data Databases - ScienceSoft

Contributors

Alex Bekker
Alex Bekker

Head of Data Analytics Department, ScienceSoft

Dmitry Kurskov

Head of Information Security Department, ScienceSoft

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.

Big data architecture - ScienceSoft

Features of Big Data Databases

Data storage

  • Storing petabytes of data.
  • Storing unstructured, semi-structured and structured data.
  • Distributed schema-agnostic big data storage.

Data model options

  • Key-value.
  • Document-oriented.
  • Graph.
  • Wide-column store.
  • Multi-model.

Data querying

  • Support for multiple concurrent queries.
  • Batch and streaming/real-time big data loading/processing.
  • Support for analytical workloads.

Database performance

  • Horizontal scaling for elastic resource setup and provisioning.
  • Automatic big data replication across multiple servers for minimized latency and strong availability (up to 99.99%).
  • On-demand and provisioned capacity modes.
  • Automated deleting of expired data from tables.

Database security and reliability

  • Big data encryption in transit and at rest.
  • User authorization and authentication.
  • Continuous and on-demand backup and restore.
  • Point-in-time restore.
  • Compliance with national, regional, and industry-specific regulations GDPR (for the EU), PDPL (for Saudi Arabia), HIPAA (for the healthcare industry).

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.

AWS DynamoDB

Description

A leader among Big Data NoSQL databases in the Forrester Wave Report.

  • Support for key-value and document data models.
  • ACID (atomicity, consistency, isolation, durability) transactions.
  • Integrations with AWS S3, AWS EMR, Amazon Redshift.
  • Microsecond latency with DynamoDB Accelerator.
  • Real-time data processing with DynamoDB Streams.
  • On-demand and provisioned read/write capacity modes.
  • End-to-end big data encryption.
  • Point-in-time recovery and on-demand backup and restore.

best for

Operational workloads, IoT, social media, gaming, ecommerce apps.

Pricing

Database operations:

  • On-demand request units (RU): $1.25/million write RU and $0.25/million read RU.
  • Provisioned capacity unit (CU): $0.00065/write CU and $0.00013/read CU.

Storage: first 25 GB/month – free, $0.25/GB/month thereafter.

More details: AWS DynamoDB

Azure Cosmos DB

Description

A leader among Big Data NoSQL databases in the Forrester Wave Report.

  • Support for the multi-model data schema.
  • Open-source APIs for SQL, MongoDB, Cassandra, Gremlin, etc.
  • Integration with Azure Synapse Analytics for real-time no-ETL analytics on operational data.
  • Support for ACID transactions.
  • On-demand and provisioned capacity modes.
  • Big data encryption (in transit and at rest) and access control.
  • 99.999% availability.

best for

Operations management, ecommerce, gaming, IoT apps.

Pricing

Database operations:

  • Provisioned throughput: 100 request units/second, single-region write account - $0.012/hour (autoscale) and $0.008/hour (manual).
  • Provisioned throughput reserved capacity: up to 65% savings.
  • Serverless (bills for the request units (RU) used for each database operation) – $0.25 for 1,000,000 RU.

Storage: 1GB consumed transactional storage (row-oriented) – $0.25/month.

More details: Azure Cosmos DB

Amazon Keyspaces

Description

  • Support for Apache CQL API code, Cassandra-licensed drivers and developer tools for running Cassandra workloads.
  • Big data encryption at rest and in transit.
  • On-demand and provisioned capacity modes.
  • Integration with Amazon CloudWatch for performance monitoring.
  • Continuous backup of table data with point-in-time recovery.
  • 99.99% availability within AWS Regions.
  • Integration with AWS Identity and Access Management for database access control.

Best for

Fleet management, industrial maintenance apps.

Pricing

Database operations:

  • On-demand throughput: $1.45/million write RU, $0.29/million read RU.
  • Provisioned throughput: write RUs - $0.00075/hour, read RUs - $0.00015/hour.

Storage: $0.30/GB/month.

More details: Amazon Keyspaces

Amazon DocumentDB

Description

  • MongoDB compatibility.
  • Support for the ACID transactions.
  • Migration support (e.g., MongoDB databases on-premises to Amazon DocumentDB) with AWS Database Migration Service.
  • Support for role-based access with built-in roles.
  • Network isolation.
  • Instance monitoring and repair.
  • Cluster snapshots.

Best for

User profiles, catalogs, and content management.

Pricing

  • On-demand instances: $0.277- $8.864/instance-hour consumed (Memory Optimized Instances Current Generation).
  • Database I/O: $0.20/1million request.
  • Database storage: $0.10/GB/month.
  • Backup storage: $0.021/GB/month.
More details: Amazon DocumentDB

Amazon Redshift

Description

  • Flexible database management platform for big data querying with SQL, a leader of Gartner Magic Quadrant for Data Management Solutions for Analytics. 
  • Automated infrastructure provisioning.
  • On-demand and provisioned capacity modes.
  • Amazon Redshift Spectrum to query big data in the data lake (Amazon S3).
  • Federated queries support for operational data querying.
  • Big data encryption (in transit and at rest).
  • Network isolation.
  • Row- and column-level security.

best for

BI and real-time operational analytics on business events.

Not suitable for Online Transaction Processing (OLTP) in milliseconds.

Pricing

  • On-demand pricing: $0.25/hour (dc2.large) - $13.04/hour (ra3.16xlarge).
  • Reserved instance pricing allows saving up to 75% over the on-demand option.
  • Managed storage pricing (for RA3 node types) –$0.024/GB/month.
More details: Amazon Redshift

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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*What is your industry?

*What data will be stored in your big data database?

?

Different big data databases are optimal for different types of data. This is the most important factor.

*What is the structure of your data?

?

Time series data is natural for sensor readings. Event data describes a variety of transactions and other events. Graphs reflect complex relationships between customers, social application or game users, industrial assets, knowledge items, etc.

*What would be the main functions of your big data database?

?

Highlight all important use cases. Use cases determine the way the read and write operations should be optimized.

*What is your current/expected data volume?

?

If you do not know your data size in TB, describe it as a number of data records, e.g., sensor readings, transactions, orders, payments, etc. in comments.

*What is expected data volume growth during the next 12 months?

*What are your data backup requirements?

?

Please leave a comment if you need a specific backup policy.

*Has your company been using any cloud services so far?

?

Additional details on cloud usage may be useful.

*Do you have any compliance requirements?

?

There is specificity in how different big data databases support compliance requirements.

Do you already have a database you want to migrate data from?

Please provide your contact details so we can send you a personalized recommendation.

We will not share your information with third parties or use it in marketing campaigns. Check our Privacy Policy for more details.

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?

Our team is on it!

Big Data Database Implementation by ScienceSoft

Since 1989 in software engineering, we rely on our mature project management practices to drive projects to their goals regardless of arising challenges, be they related to time and budget constraints or changing requirements.

Big data consulting

We offer:

  • Big data storage, processing, and analytics needs analysis.
  • Big data solution architecture.
  • An outline of the optimal big data solution technology stack.
  • Recommendations on big data quality management and big data security.
  • Big data databases admin training.
  • Proof of concept (for complex projects).
Go for consulting

Big data database implementation

Our team takes on:

  • Big data storage and processing needs analysis
  • Big data solution architecture.
  • Big data database integration (integration with big data source systems, a data lake, DWH, ML software, big data analysis and reporting software, etc.).
  • Big data governance procedures setup (big data quality, security, etc.)
  • Admin and user training.
  • Big data database support (if required).
Go for implementation

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

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What makes ScienceSoft different

We achieve project success no matter what

ScienceSoft does not pass mere project administration off as 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 it

About ScienceSoft

ScienceSoft is a global IT consulting and IT service provider headquartered in McKinney, TX, US. Since 2013, we offer a full range of big data services to help companies select suitable big data software, integrate it into the existing big data environment, and support big data analytics workflows. Being ISO 9001 and ISO 27001-certified, we rely on a mature quality management system and guarantee cooperation with us does not pose any risks to our clients’ data security.