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Enterprise Data Storage
Architecture, Tech Stack, Costs
In data warehousing services since 2005, ScienceSoft builds scalable and secure data storage solutions for enterprises in BFSI, healthcare, retail, manufacturing, logistics, and 20+ other industries.
Contributors
Head of Data Analytics Department, ScienceSoft
Business Analyst and BI Consultant, ScienceSoft
Enterprise Data Storage: The Gist
According to a recent BARC Data Culture Survey, most enterprises use a combination of several data storage and access technologies, with data warehouses (DWH) and data lakes being among the most popular options.
Why enterprises are adopting data lakes
Businesses now leverage data for much more than just historical analytics and reporting. They want to use their data for advanced operations like dynamic process optimization, real-time fraud detection, and predictive modeling, which are often driven by ML/AI engines and big data techs.
To handle these needs, we build hybrid solutions with dedicated repositories. For example, a large hospital that needs to centralize diverse clinical data like patient records, medical images, test and research data will likely benefit from a combination of a data warehouse and a data lake. DWH is optimal for structuring data for historical analytics and reporting, such as patients’ medical history and progress tracking. A data lake, in turn, would ensure cost-efficient storage of data in its raw format until it is needed, say, to build and train ML models for advanced disease progression and risk prediction or for training students and residents.
High-Level Architecture of an Enterprise Data Storage Solution
Enterprise data storage is needed to securely accumulate business information in a centralized location that is optimized for data sharing, analytics, reporting, real-time operations, and regulatory compliance. Below, ScienceSoft's software engineering experts describe key architecture elements and data flows of a sample solution that stores both raw and structured data, meeting a variety of users’ needs.
Depending on the source, data can be ingested into the data lake via a message bus (for enterprise systems and third-party services like ERP, CRM, EHR, or an ecommerce platform) or an API (for third-party data sources like payment gateways and messaging services).
- The repository that is optimized for cost-efficient storage of raw data in its initial format (e.g., TXT, PDF, CSV, JSON, Parquet, MP3, MP4).
- Enables primary data normalization in the staging zone (e.g., filtering out erroneous sensor readings).
- Serves as an optimal environment for building and training ML/AI models. Data scientists have access to large amounts of historical data and can run experiments in the analytics sandbox that is isolated from the rest of the ecosystem and doesn't affect its performance or data integrity.
- Features highly structured analytics-ready data that was filtered, deduplicated, standardized, and otherwise cleaned during processing and is organized according to the defined storage format (e.g., rows and columns, tags for data elements identification, key-value pairs).
- Enables enterprise-wide business intelligence (BI) with the help of data marts — DWH subsets that feature dimensions and measures relevant to the specific needs of different business departments (e.g., for sales, HR, financial, operational metrics).
The data governance framework defines data quality standards, metadata management, retention policies, access controls, and compliance requirements. It usually enforces mechanisms such as data encryption at rest and in transit, role-based access control, multi-factor authentication, data backup and recovery, data privacy controls (e.g., data masking, anonymization, pseudonymization), and more.
Techs and Tools to Build an Enterprise Data Storage Solution
See How Our Clients Use Enterprise Data Storage for BI and Analytics
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.
Consolidated Enterprise Data Storage Drives up to 30% Higher Productivity for Analytics Teams
The figure is featured in the IDC research of business value driven by popular enterprise storage software (Amazon Redshift Cloud Data Warehouse and Oracle Autonomous Data Warehouse). The increase in productivity is associated with highly structured enterprise data that allows for data exploration by non-IT users. The study spans organizations in the pharmaceutical, finance, energy, manufacturing, professional services, retail, real estate, telecommunications, and advertising industries. The surveyed companies either did not have centralized data storage before implementing data warehouses or replaced their legacy storage solutions with AWS and Oracle techs.
Estimate the Cost of Your Enterprise Data Storage Solution
The cost of implementing an enterprise data storage solution may vary from $30,000 to $1,000,000+. Some of the cost factors include data volume and complexity, the number and nature of data sources for integration, the need to support advanced capabilities like ML/AI-powered and big data analytics. Use our online calculator to get a tailored estimate or visit our dedicated page to see more detailed cost ranges and learn what makes up DWH implementation costs.
Get a ballpark cost estimate for your enterprise data storage solution.