SoftKraft is a data engineering company for CTOs and founders. We design and build cloud data platforms on AWS and GCP — ingestion, warehousing, governance — and deliver production pipelines up to 90% faster than typical greenfield internal builds.
Architecture, pipelines, governance, and cost control for B2B teams preparing data for analytics, BI, and AI on AWS and GCP.
Our experts assess the project you're planning or review your existing deployment — design trade-offs, best practices, and pitfalls before your team commits to build.
We implement lakehouse and warehouse architectures on AWS (Redshift, S3, Athena) and GCP (BigQuery, Pub/Sub). See our Python data pipeline architecture guide for the stack we use in production.
We build governance foundations — lineage, access control, quality rules — aligned with GDPR, CCPA, and your industry requirements. Automate data pipelines before scaling AI workloads.
We tune storage tiers, query patterns, and processing schedules on cloud-native data platforms so you pay for performance where it matters — not idle capacity.
Every production AI system — from BI dashboards to RAG agents — depends on clean, governed, accessible data. Assess your data maturity for AI first; then see our AI development services for the next step.
Real estate ETL, gaming analytics, and BI dashboards — production pipelines our teams designed and shipped.
Apache Spark’s big data processing speeded up investment decision-making and provided scalable infrastructure for growing data volumes and machine learning solutions
Using Apache Kafka and OpenShift to build a real-time streaming solution enabling gamers to optimize their performance and monetization strategies in a gaming community
Installing Big Data-enabled BI Tool in company IT infrastructure allowed analysts to interactively examine business data in near real time as well as equipped them to make faster and better investment decisions
We connect architecture, scope, and delivery choices to the business result your team needs—not simply to completed tasks.
We take responsibility for the decisions made during development and keep delivery transparent through clear communication and collaboration.
More than 80% of our business comes from long-term partnerships, built by delivering quality outcomes and adapting as priorities change.
SoftKraft have proven are way ahead of the curve. The team impressed us with their ability to speak at the business strategy level.
SoftKraft has been a very reliable partner for us. They took over our system infrastructure in a short time and managed to handle it in a professional and reliable way.
We were very impressed with their commitment to achieving a high-quality outcome and their willingness to explore a variety of possible solutions for our goal.
Assess your current stack, data sources, and AI/BI goals — with a free initial consultation.
Select warehouse or lake architecture, orchestration (Airflow), and streaming (Kafka) where real-time data is required.
Deliver ingestion, transformation, quality checks, and observability for production workloads.
Document access control, lineage, and operating procedures so your team can run and extend the platform.
Data Engineering and Data Science are complementary disciplines.
Data Engineering prepares, secures, and moves data so data scientists and AI systems can use it reliably. Data engineers own ingestion, ETL/ELT, cleansing, storage, and pipeline orchestration.
Data Science combines statistics, mathematics, and machine learning to extract insights and build predictive models on top of governed data.
Start with a free consultation — we'll review your current data architecture and goals. From there, you choose the engagement model that fits:
Production data platforms follow a layered architecture — ingestion, storage, processing, and visualization — adapted to your cloud stack and access patterns:
We handle structured, unstructured, and semi-structured streams via real-time (Kafka, Kinesis, Pub/Sub) or batch jobs, prioritizing and categorizing data before it enters downstream layers.
Raw and processed data lands in cloud storage and warehouse services aligned with your query patterns — S3, Redshift, BigQuery, or lakehouse configurations on AWS and GCP.
The processing layer selects, cleans, and formats data for analysis and modeling. We use Spark, Airflow-orchestrated pipelines, and cloud-native ETL/ELT services.
We integrate BI tools such as Amazon QuickSight or Tableau so leadership teams can act on analyzed data. See Embedded Analytics: Amazon QuickSight vs Tableau for a comparison.
