IntelliAI + Google Cloud = Quantum Leap In Business Agility & Insights
Tap into our deep Data Warehouse & ML expertise for a serverless
highly scalable, cost-effective multi-cloud data warehouse solution
Your Trusted Partner for Enterprise Data Warehousing & Built-In Machine Learning
Fast, highly scalable, cost-effective, and fully managed cloud data warehouse
Google BigQuery is multi-cloud cloud native data warehouse on Google Cloud designed for business agility. BigQuery solution is completely serverless, self-scaling, self-maintaining, self-tuning and cost-effective. There are no nodes to plan, configure, or scale. The complexity of sizing, managing, and maintaining the physical infrastructure is handled by Google. End-users gain the benefit of all the auto-tuned and optimized resources working simultaneously. Customers can either pay by the total amount of data processed per month, or opt to pay a flat-rate fee based on number of “slots” (effectively the number of parallel queries) made available. While slots can be allocated to a particular department to process queries, unused slots may be allocated to other departments to handle bursts, optimizing slot utilization. Improvements to optimize queries are being added monthly with a goal of shortening query execution time & minimizing the amount of data processed, thus minimizing on-demand cost to the end user.
Data Warehouse Cloud Strategies
For strategy options, we evaluate moving data warehouse (DWH) to cloud from both technical & economic perspectives, and evaluate DWH in the cloud vs. cloud-native DWH architectures. Compare TCO of running on premises vs. as cloud-native DWH.
Data Warehouse Modernization
Upgrade, redesign & reimplement DWH in the cloud to give them future-facing interoperability, speed, capacity, and analytics. There are usually many streams at modernization that occur concurrently and these have project dependencies.
Migrating your Data Warehouse to Cloud
Gain greater business innovation, agility, speed, scale, & flexibility with migration to cloud. We organize data warehouse migration to cloud as a multi-phase project which is easier to manage and execute, and with reduced risks.
BigQuery ML
BigQuery ML enables data scientists & analysts to build & operationalize ML models on planet-scale structured or semi-structured data, directly inside BigQuery, using simple SQL in fraction of time. Export ML models to Cloud AI Platform or your serving layer for prediction
Our Work With Partners
BigQuery ML, BI Engine and GIS
BigQuery ML enables to build ML models inside BigQuery. BigQuery BI engine is fast, in-memory analysis service, allows analysis of large complex datasets with sub-sec response time. BigQuery GIS has serverless architecture of BigQuery with native support for geospatial analysis.
Data Integration
We can pull together data from your several disparate data sources to one centralized data warehouse Google BigQuery, and automate this process.
Data Marts
BigQuery has Datasets which are collections of tables that can be divided along your business lines or your analytical domain. We can use this data marts feature for better reporting and visualization to all stakeholders.
Hadoop & Spark Migration to Google Cloud
We modernize and migrate your Hadoop and Spark infrastructure to Google Cloud to reap all the cloud benefits.
Legacy EDW Migration to Google Cloud
We move your data from legacy enterprise data warehouses, such as InfluxDB, Teradata and Netezza, to Google Cloud.
Other Cloud Migration to Google Cloud
We move your data from other cloud providers' data warehouses, such as AWS Redshift, to Google Cloud.
Development and Testing
Process Redesign
Migration Strategy & Roadmap for EDW
Count on our build expertise. Some of the key considerations include re-tooling applications with cloud native tools, transforming data into new schemas, rewriting optimized queries, testing, validation, creating custom apps, retraining developers on cloud tools.
Every change presents an opportunity to improve & redesign many processes & systems, such as changes to ETL process, event driven integration, streaming capabilities, integration with cloud products natively, educating business & IT teams on new process & tools.
Our strategy is either to migrate data all at once & flip the switch on existing operations, or migrate operations slowly over a period of time while phasing out the on-premise EDW. We evaluate migration & modernization based on time, transfer of data, potential downtime, etc.
Data Warehouse Cloud Strategies
Data security and governance are two key areas to consider for your data warehouse:
- Is your data always encrypted at rest and in transit?
- Is fine-grained, role-based access control supported?
- Is transparent audit logging for activity, data access, and billing supported?
Consider if / how the data warehouse increases your organization's ability to contribute to strategic initiatives of business:
- scale seamlessly and simplify your operational burdens?
- allow teams to share & collaborate easily, across data artifacts & analysis?
- automate data delivery of new sources demanded by business?
- lay a strong foundation for predictive analytics & machine intelligence?
Data Warehouse Modernization
Modernization takes place concurrently across several key initiatives in an organization:
- Business Modernization leads the way by deciding how to modernize the business to keep pace and stay relevant with evolving customers, partners, marketplaces, and economies.
- Analytics Modernization drives data warehouse modernization because warehouses are primarily built for analytics and reporting, and therefore need to be extended or redesigned to accommodate the new data requirements of advanced analytics in the organization.
- Data Platform Modernization is pre-requisite for modernizing the data in data warehouse for appropriate storage, capacity, interfaces, in-place processing, & multi-structured data support. This is to embrace data dimensionality, real time & unstructured data, & many sources for analytics.
- Report Modernization requires adopting newer tools for dashboarding, data visualization, & data exploration.
Migrating your Data Warehouse to Cloud
We prioritize high-value components, such as business analytics, that should be migrated during the early phases.
Cloud data warehouses, such as Google BigQuery with built-in BigQuery ML, that truly accelerate organizations have following important characteristics:
- Serverless computing to free IT to contribute to strategic business initiatives leveraging data, rather than spending time on data management.
- Separation of storage and compute resources to allow organizations to achieve a full, actionable view of their data estates without compromising on performance. This reduces risk, leading to increased success of analytically driven business initiatives.
- Strong integration with emerging machine intelligence systems to allow organizations to quickly provide predictive analytics.
BigQuery ML
Build, test, and operationalize custom machine learning models in BigQuery using familiar SQL language. In addition, you can also use Connected Sheets to analyze billions of rows of live BigQuery data in Google Sheets without requiring SQL knowledge.
Create machine learning models in minutes directly inside BigQuery without extensive data sampling or moving data out of the data warehouse.
Drive product recommendations, segmentations, and predictions at petabyte scale, and at a fraction of the cost and time.
For online prediction, BigQuery ML models can easily be exported to Cloud AI Platform or your own serving layer.
Our Expertise
Methodology For Path To Cloud
Design And Build Considerations
Let Us Chat!!
IntelliAI helps you migrate to data warehouse in the cloud, or cloud-native data warehouse - BigQuery - a serverless, highly scalable data warehouse with built-in ML
Economic Value – Google BigQuery vs Legacy EDW Solution
BigQuery provides significant tangible benefits and capital & operational savings when compared with leading legacy EDW solutions deployed either on-premises or on cloud, as follows:
- Elimination of up-front investment: BigQuery’s serverless design is billed monthly and eliminates the need to make up-front investments in hardware or sign annual contracts to reduce the cloud spend.
- Elimination of on-premises related operational expenses: BigQuery eliminates the need to spend on power, cooling, floorspace, license management, maintenance,and hardware & software upgrades.
- Reduction in cost of daily administration: BigQuery eliminates the need to manage EDW nodes, as well as the need to monitor, update, tune, & plan for growth, database administration, ETL management, schema modification
- Improved agility, scalability, availability and built-in ML: BigQuery scales up or down as needed to meet changing business demands, enabling organizations to quickly act on new opportunities, and perform queries leveraging built-in machine learning models.
- BI Engine: BigQuery also provides a BI engine which is blazing-fast in-memory analysis service for BigQuery that allows analysis of large & complex datasets interactively with sub-second query response time.
- Capability to analyze without SQL knowledge: BigQuery provides Connected Sheets which analyze billions of rows of live BigQuery data in Google Sheets without requiring SQL knowledge, with familiar tools.
Our Google Solutions
