Ordinal Science



Big Data Consulting

Ordinal Science helps clients architect a cloud platform to handle scalable data loads. We design and implement migration plans from legacy platforms to high throughput databases and streaming analytics in colocation data centers or AWS cloud services.

Solve performance bottlenecks with Kafka or Kinesis Streaming, handle IoT volumes with NoSql databases, accelerate analytics with Spark, glean more insight with machine learning.

Case Study

Client Challenge

Our client utilized a solid RDBMS system with 100+ tables in colocation data centers. But rapidly growing databases put a strain on the client’s DevOps team. The burden of tuning the system, maintaining uptime and backups, provisioning new servers to handle the increasing volume disabled new development initiatives.

Ordinal Science Approach

We recommended a managed RDBMS on Amazon Web Services and proposed a migration plan. Our team modified 500+ stored procedures for efficiencies of the new database engine, adopted data migration tools, conducted full scale testing, then executed migration with only 3 seconds of total downtime.

The Result

The migration process was complete in sprints totaling six months in duration. The impact on the client’s customers was minimal during the migration process. And the migration has achieved the primary objective of reducing the database management and maintenance by 75 percent, freeing the DevOps team for other projects.

Ordinal Science Services

Ordinal Science

Infrastructure Assessment

Identify data pipeline bottlenecks
Assess storage and access strategies
Recommend incremental improvements
Design comprehensive data plan

Ordinal Science

Software Development

Design and develop software
Build complete data pipelines
Write machine learning platforms
Develop analytics for big data

Ordinal Science

Implementation Support

Design and execute migrations
Evaluate and clean data
Train machine learning systems
Troubleshoot big data implementation

Consulting Focus



Design scalable technology mix
Migrate to cluster technology
Shift from co-lo to cloud services
Integrate ETL into reporting

Internet of Things

Internet of Things

Design IoT business case
Develop ingestion endpoints
Implement data acquisition
Integrate real-time analytics


Machine Learning (ML)

Design ML use cases
Develop ML software
Connect to data pipelines
Train and validate results

Technologies We Use

AWS Services

Kinesis Firehose and Streams
Elastic Map Reduce, Hadoop
Aurora, Redshift
EC2, S3, EFS

Open Source Platforms

Apache Spark Analytics,
Kafka Streaming
TensorFlow, Caffe, MLLib,
MySQL, Galera, Mongo

Development Tools

Java, Python, C#
Bitbucket, Github
Eclipse, IntelliJ
Bitbucket Pipelines, Jenkins

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