datasops logodatasops

Data · Consulting · Engineering · AI · Software

Engineer Your Data.
Accelerate Your Business.

We help companies harness the power of Elastic Stack, Generative AI, and intelligent automation to transform raw data into operational advantage.

Get StartedSee Our Work

Technologies we partner with

ElasticOpenAIGrafanaLangChainKubernetesDockerConfluentDatabricksTerraformOracleRedisElasticOpenAIGrafanaLangChainKubernetesDockerConfluentDatabricksTerraformOracleRedis

What We Do

End-to-End Data & AI Solutions

From observability to AI integration, we cover the full spectrum of modern data operations.

Elastic Stack & Observability

ELK stack deployment, log analytics, APM, SIEM, and real-time monitoring dashboards for complete system visibility.

LLM Integration & Strategy

Custom LLM deployment, A2A architectures, RAG pipelines, context engineering, and model evaluation for your use cases.

Generative AI Solutions

AI-powered content generation, intelligent document processing, AI agents, chatbots, and copilots.

Business Applications & Microservices

Enterprise apps, microservices, event-driven and distributed architecture, full-stack systems end to end.

Business Process Automation

Workflow automation, ETL pipelines, system integration, CI/CD, and infrastructure as code.

Data Consulting & Architecture

Data strategy, architecture design, migration planning, compliance audits, and team enablement.

How We Work

From Discovery to Delivery

A proven process that minimizes risk and maximizes value at every stage.

01

Discover

Free

We audit your current stack, identify gaps, and map out opportunities. You get a clear picture of where you stand and where to go.

02

Architect

Week 1

We design the solution, define the roadmap, and align on timelines and costs. No surprises, full transparency.

03

Build

Weeks 2-6

Agile implementation with iterative delivery. You see progress every week and can steer the direction as we go.

04

Optimize

Ongoing

We monitor, measure, and continuously improve. Your systems evolve as your business grows.

0+
Projects Delivered
0+
Clients
0%
Avg Cost Reduction
<0h
Response Time

Featured Work

Real Results, Real Impact

See how we have helped companies transform their data operations and accelerate growth.

SaaSApplicationsAI

FitSpot.io — All-in-One Platform for Personal Trainers

Built a full SaaS product with scheduling, CRM, AI workout builder, analytics, and payments — replacing spreadsheets for 100+ trainers.

10,000+ workouts managed
ObservabilityGrafanaInfluxDB

Real-Time Observability Platform — Company XYZ

Deployed a production-grade Grafana + InfluxDB monitoring stack processing 2TB+ daily across a distributed microservices architecture.

10x faster incident detection

Technologies

Our Technology Stack

We work with the best tools in the industry to deliver robust, scalable solutions.

Search & Analytics

ElasticsearchLogstashKibanaBeatsGrafanaPrometheus

AI & Machine Learning

OpenAILangChainHugging Face

Platforms & Infrastructure

KubernetesOpenShiftDockerTerraformAWSGCPLinuxNginxHAProxy

DevOps & CI/CD

GitHub ActionsArgoCDAnsibleJenkinsGitLab CIHelm

Data & Streaming

PostgreSQLOracleRedisApache KafkaAirflow

Development

JavaSpring BootTypeScriptPythonRustReactNext.jsNode.js

Testimonials

What Our Clients Say

“datasops built us a complete training and gym management system that replaced five different tools we were juggling. My trainers save hours every week and clients love the app.”

Marcin

Gym Owner

“We needed a system that could handle the chaos of water damage restoration — site photos, moisture readings, technical reports, insurance docs, all in one place. datasops built us a custom CRM that ties it all together. Now every job is documented from first call to final sign-off.”

Grzegorz

Owner, Structural Drying Company

“The automation workflows datasops built saved us 200+ hours per month. Best technology investment we’ve made this year.”

Piotr

Director of Operations

Who We Are

Meet the Founders

Three engineers who believe great technology consulting starts with deep technical expertise and honest advice.

MR

Mateusz Rybak

Co-Founder

Specializes in software development and distributed systems. Turns complex requirements into production-ready systems.

PS

Patryk Sikora

Co-Founder

Expert in Elastic Stack, infrastructure, and AI integration. Builds data platforms and observability solutions at enterprise scale.

AS

Arkadiusz Sieczak

Co-Founder

DevOps Engineer turning complex infrastructure into automated, reliable systems — from bare metal to GKE. Passionate about open-source, with a research background in blockchain and cybersecurity.

From the blog

Latest insights

Deep-dives on data engineering, observability, and AI in production.

pgvectorPostgreSQLVector Search

Vector Search with pgvector — Similarity Search, HNSW Indexing, and Production Patterns

A comprehensive guide to pgvector in production: installing the pgvector extension on PostgreSQL and choosing between IVFFlat and HNSW approximate nearest neighbour indexes with a detailed comparison of build time, query latency, recall, and incremental insert behaviour, generating and storing embeddings from OpenAI text-embedding-3-small and sentence-transformers with batched upserts using execute_values for high-throughput ingestion, cosine distance and L2 distance operators with indexed ORDER BY queries, filtered k-NN search with WHERE clauses and partial HNSW indexes scoped to specific tenants or workspaces, hybrid search combining vector similarity with BM25 full-text ranking via Reciprocal Rank Fusion for keyword-plus-semantic retrieval, Python integration with psycopg2, asyncpg, and SQLAlchemy using the pgvector-python adapter for zero-overhead vector serialisation, connection pooling with PgBouncer in transaction mode and per-transaction SET LOCAL for ANN search parameters, a complete RAG retrieval pipeline embedding user queries and fetching top-k chunks with similarity thresholds, HNSW index maintenance with REINDEX CONCURRENTLY and autovacuum tuning for high-write vector tables, and a decision framework comparing pgvector against dedicated vector databases including Qdrant, Pinecone, and Weaviate across vector count, query latency, operational overhead, metadata filtering expressiveness, ACID consistency, and cost.

Read more
DuckDBSQLAnalytics

DuckDB for Analytical Workloads — Columnar SQL, Arrow Integration, and In-Process Analytics

A comprehensive guide to DuckDB for analytics: in-process columnar SQL engine with no server overhead, direct scanning of Parquet, CSV, and JSON from local disk and S3 with httpfs extension and automatic predicate pushdown, Apache Arrow zero-copy integration with Pandas and Polars via the C Data Interface for sub-millisecond DataFrame interop, window functions and complex aggregations with QUALIFY, UNNEST, and PIVOT syntax, parallel multi-core execution with configurable memory limits and streaming out-of-core spill, extension ecosystem including delta for Delta Lake reads, iceberg for Apache Iceberg table scanning, httpfs for S3 and GCS object storage, and spatial for geospatial SQL, dbt-duckdb adapter for fast local development and CI builds without cloud warehouse credentials, MotherDuck cloud service for team collaboration and transparent hybrid execution joining local and remote tables, and a decision framework comparing DuckDB against pandas and Apache Spark across dataset size, concurrency, and operational complexity dimensions.

Read more
MLOpsCI/CDMachine Learning

MLOps CI/CD — Automating Model Training, Validation, and Deployment Pipelines

A practical guide to MLOps CI/CD: reproducible training pipelines with DVC and MLflow that version data alongside code and log every experiment to a central tracking server, statistical evaluation gates comparing challenger models against the champion using bootstrap confidence intervals on AUC differences and per-segment fairness checks that block promotion on regression, MLflow Model Registry lifecycle stages (None → Staging → Production) with automated gate transitions and human approval for production promotion, full GitHub Actions workflow for training, evaluation, and canary deployment triggered on code push and nightly schedule, KServe InferenceService canary traffic splitting with progressive rollout and automated revert on alerting rule fire, shadow mode deployment for zero-user-impact validation of serving skew before any live traffic, production monitoring with Evidently AI for data drift detection and automated retraining dispatch via GitHub Actions workflow_dispatch, and a 10-point MLOps CI/CD production checklist covering data versioning, evaluation gates, serving environment parity, and drift-triggered retraining with cooldown enforcement.

Read more
Data MeshData ArchitectureDomain Ownership

Data Mesh in Practice — Domain Ownership, Data Products, and Federated Governance

A practical guide to implementing Data Mesh in production organizations: identifying data domain boundaries using bounded context principles and the first-to-know heuristic, assigning domain ownership so the team that generates data is accountable for its quality, designing data products as independently deployable units with versioned Avro schemas, explicit SLO manifests (freshness ≤30 min, completeness ≥99.5%), and discoverable catalog entries, building a self-serve data platform with opinionated Terraform modules for BigQuery output ports, dbt project templates with pre-configured CI/CD and freshness tests, and automated catalog registration on deploy, federated computational governance with policy-as-code CI checks for schema backward compatibility, PII column tagging, and SLO threshold bounds, implementing a production data product end-to-end with dbt staging/intermediate/product layers, Avro schema registry integration, and declarative dbt tests for uniqueness, freshness, and referential integrity, and measuring Data Mesh adoption maturity with DORA-inspired metrics: deployment frequency, lead time, change failure rate, and MTTR emitted to OpenTelemetry.

Read more
Apache SparkPerformanceData Engineering

Apache Spark Performance Tuning — Partitioning, Caching, Joins, and Query Planning

A comprehensive guide to Apache Spark performance tuning in production: Catalyst optimizer phases and physical query plan analysis with EXPLAIN FORMATTED, partition sizing with repartition() vs coalesce() and detecting data skew via Spark UI task duration distributions, broadcast hash joins with autoBroadcastJoinThreshold and explicit broadcast() hints, sort-merge join elimination with bucketed writes, AQE skew join splitting with skewedPartitionFactor and salting for non-join aggregations, RDD persistence levels (MEMORY_AND_DISK_SER, OFF_HEAP) with cache-aware pipeline patterns, executor memory anatomy (heap + overhead + PySpark worker) and GC pressure diagnosis, shuffle optimization with spark.sql.shuffle.partitions and Adaptive Query Execution auto-coalesce, Parquet and Delta Lake file format tuning with Z-ordering, file compaction, and sorted writes for row-group skip, and production configuration recipes for EMR, Databricks, and on-premises YARN clusters.

Read more
dbtData QualityTesting

dbt Testing Strategies — Unit Tests, Schema Tests, and Data Quality Assertions in Production

A comprehensive guide to dbt testing in production: built-in generic tests (unique, not_null, accepted_values, relationships) with severity thresholds, singular SQL tests for custom multi-column business logic assertions, dbt unit tests introduced in dbt 1.8 with inline fixture data for testing CASE expressions and window functions in isolation, custom generic test macros in Jinja2 for reusable parameterized assertions, dbt-utils and dbt-expectations packages for statistical bounds, cardinality checks, regex validation, and cross-table row count comparisons, source freshness checks with loaded_at_field and warn/error thresholds for detecting stale ingestion, test severity configuration with warn_if and error_if row count thresholds, test selection with --select state:modified+ and --defer for slim CI on changed models, a layered test strategy across staging/intermediate/marts that matches test density to risk, and CI/CD integration with GitHub Actions for source freshness gating, slim CI builds, and historical test result tracking via run_results.json.

Read more
View all articles

FAQ

Frequently Asked Questions

Everything you need to know about working with us.

We work across a wide range of industries including finance, healthcare, e-commerce, logistics, and telecommunications. Our solutions are tailored to each client’s specific domain requirements and regulatory environment.

It depends on the scope. A focused observability deployment or automation workflow can be delivered in 4-6 weeks. Larger initiatives like full-scale LLM integration or platform builds typically run 2-4 months. We always start with a discovery phase to align on timelines.

Yes. We offer flexible support and maintenance plans to ensure your systems stay healthy, updated, and optimized. We can also embed with your team on a part-time basis for continuous improvement.

Absolutely. We integrate with your current infrastructure and tools rather than forcing a rip-and-replace. Whether you’re on AWS, GCP, Azure, or on-prem, we adapt our approach to what works best for your environment.

We offer both fixed-price project engagements and time-and-materials contracts depending on the nature of the work. Reach out through our contact form and we’ll provide a tailored estimate within 24 hours.

Security is built into every engagement. We follow industry best practices for data handling, support GDPR and SOC 2 compliance requirements, and can work within your existing security policies and access controls.

Get in Touch

Send us a message

Tell us about your project and we’ll get back to you within 24 hours with actionable next steps.

Prefer e-mail?

hello@datasops.com

We will respond to you within 24 hours.