Modernize Your Legacy Data Architecture
or Build New Data and Analytics Capability from Scratch
The development of a modern data warehousing infrastructure needn’t cost millions of dollars. We deliver ready-to-use custom business solutions at a reasonable price, over a period from just a few weeks, up to six months.
Business does not have data or powerful processing tools to hand;
Data is not integrated, cleansed, described and not accessible or useful for the business;
The need to make user-friendly digital services available for your customers, at any time and place, is impeded by the inability to support digital services with data and analytics;
Data consolidation in a conceptually centralized location cannot be achieved on the legacy platform, due to limitations of legacy ETL tools, limited data storage and compute power scalability;
Business does not have the ability to utilize data science, machine learning and real-time analytics.
BI cases take a long time to implement. Business users have to wait for weeks before they see a report with new data, even if they need to see it today;
There is no support for semi-structured data and your business has to wait before the engineering team develops a new data model and ETL for each new dataset;
You hit challenges of scale. Business users have to wait before they can run their workloads, data is delayed, contention for DWH resources becomes a frustration for users. Storage and compute power of your legacy solution have to be scaled together, which makes it very expensive, and there is no elasticity in your DWH resource even when your workloads are very uneven;
Data quality and confidence are low due to patchy data quality practices and lack of lineage and transparency of end-to-end data flows;
Data potential stays locked. Large volumes of valuable historical data reside in legacy systems;
Quick fixes, workarounds, and delayed upgrades result in accumulated technical debt, requiring enhanced budgets and impacting the speed of change.
The old-school model of paying for hardware, licenses, upgrades, and managed services is less efficient and flexible than cloud-based models, offering ultra-low maintenance, simplicity, elasticity, separate scalability of computing and storage, and easy integration with the existing ecosystem of tools;
Contracts with existing vendors supporting legacy data platforms expire;
A legacy data warehouse requires significant management.
While the cost of regulatory compliance might be substantial, non-compliance will likely be even more expensive
Your business needs to comply with security and governance requirements
We’ll provide our recommendations on the target data architecture, implementation and migration approach (data architecture that is appropriate for your goals, use cases/workloads and organizational aspects, data warehouse/lake, cloud, technology options, type of migration).
We’ll deliver detailed recommendations on the target data analytics architecture, security considerations, high-priority use cases/workloads, cost perspective, and implementation roadmap with estimates.
It may include cloud-based infrastructure and toolsets of data warehouses and lakes, accounts, security, governance, lineage, ELT, data transformation and integration, BI, analytics, and visualizations.
The development of a modern data warehousing infrastructure needn’t cost millions of dollars. We deliver ready-to-use custom business solutions at a reasonable price, over a period from just a few weeks, up to six months.
Analytics and reporting solution for global music streaming (Spotify, Apple, Google Music, YouTube, etc.)
Snowflake is mainly used for analytical workloads as part of a complete Data Lake solution
Existing solution (Redshift) was too costly; Snowflake was considered as an alternative
Operational DB: Amazon Aurora
Two 24x7 instances of SF as ODS
20B records in tables (22 TB)
2.5B records fact table
Python as ETL, Looker as BI
Structured and unstructured data
Fast provisioning and fast dynamic scaling allows for great elasticity and cost savings (~2x cost saving vs. Redshift)
“Time travel” feature: the ability to request data as of any point in history
years in business
full-time staff
offices across the globe
projects annually
return clients
billable hours