Data engineering teams enable data-driven decision making and empower businesses with machine learning and AI capabilities. They specialize in building modern data stacks that leverage a single data platform to collect, transform, and publish data. Allowing you to unlock the true revenue generating potential of data transformations.
Modern enterprises require accessible, usable, and actionable data for professionals throughout the organization. Breaking down data silos and providing seamless access empowers everyone to make informed decisions and drive innovation.
Whether it’s enabling data-driven decision-making via an enterprise single source of truth, empowering business users to make reliable AI/ML applications, leveraging automation to accelerated data projects, or committing to robust security practices and governance standards, phData has the expertise to be a trusted partner in building your data engineering solution.
PhData provides a variety of services to assist you in addressing common data engineering challenges that may be both difficult and expensive.
Our team of specialists has vast experience working with modern data stacks, with a focus on data warehouse migrations, technology implementation, cloud infrastructure, and network security. We have developed an extensive range of migration automation software that streamlines legacy data warehouse migrations. Our global delivery team ensures seamless integration of our solutions and experts to meet your business requirements and deadlines.
At phData, we believe in applying software engineering practices to data engineering and data science to deliver confidence and ensure trust in the outcome.
We combine our model with a global delivery team, allowing choice around a range of on-shore, near-shore, off-shore, or hybrid options to suit your budget, timeline, and specific requirements.
Ingest data from internal and external sources, real-time or batch.
Catalog data ensures it is secured, governed, and compliant.
Monitor data quality and performance.
Cleanse & Enrich data to increase its value and usability.
Model data to meet the needs of various data stakeholders.
Manage workflows for scheduling, audit, and performance.
Analyze data through tooling for analytics, machine learning, and AI.
Serve data to applications, processes, or people.
Govern the catalog of data, dictionaries, and mappings.
IT and data management teams who seek to deliver reliable experience for analytics and ML users.
Business teams understanding the impact of making data-driven decisions.
Product owners wanting to monetize data through analytics and machine learning.
Data scientists needing reliable access to data in all its forms.
Governance and security teams striving to enable both innovation and compliance across the organization.
phData specializes in data and machine learning technologies. We have deep experience and expertise in Snowflake, AWS, and Azure, as well as common legacy systems like Spark,Teradata, Oracle, MS-SQL, Netezza and Hadoop data ecosystems. That said, each of these ecosystems are broad, with various capabilities that we do and do not recommend. As an organization, we’ve built best practices around each of these technologies. Our customers build with us because engineers come with a viewpoint drawn from experience with thousands of data projects.
Design and architecture are often part of our data engineering engagements, but also often a part of data strategy. For complex or large enterprise projects, we typically recommend a dedicated engagement.
No two data engineering projects are alike, but data engineering projects can be as little as $20k-$50k for a POC or pilot. Using our pre-built automation, we’ve implemented several platform builds and legacy migrations for under $100k. That price goes up as the number of sources and requirements increases.
Are you still sinking your IT budget into Oracle licensing fees? Check out our step-by-step playbook to help you migrate to the
Snowflake Data Cloud and save costs.
Learn how phData can help solve your most challenging data engineering challenges.
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