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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.

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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.

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Projects Delivered
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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.

HashiCorp VaultSecrets ManagementDynamic Secrets

HashiCorp Vault for Data Teams — Dynamic Secrets, PKI, and Database Credential Rotation

A practical guide to running HashiCorp Vault as the secrets backbone of a data platform rather than storing warehouse passwords, connection strings and API tokens in environment files and checked-in config: the difference between a static secret that Vault encrypts, versions and audits but does not change and a dynamic secret that does not exist until something asks for it, is handed out attached to a lease and is revoked automatically when the lease ends because all dynamic secrets are required to have a lease; the database secrets engine enabled with vault secrets enable database and pointed at a database via vault write database/config with plugin_name set to postgresql-database-plugin, a connection_url templated with {{username}} and {{password}}, allowed_roles as the guardrail and a dedicated root account you immediately rotate with vault write -force database/rotate-root; dynamic roles created with vault write database/roles carrying creation_statements that CREATE ROLE with LOGIN PASSWORD and VALID UNTIL '{{expiration}}', default_ttl and max_ttl so vault read database/creds returns a fresh per-request user with lease_id, lease_duration, username and password; static roles that map one Vault role to an existing username and rotate its password on a rotation_period or a cron rotation_schedule that are mutually exclusive, read back through vault read database/static-creds, with the warning never to point a static role at root credentials because Vault cannot tell root from a managed user; leases and TTLs that make revocation a first-class operation with vault lease renew -increment, vault lease revoke by id and the break-glass vault lease revoke -prefix; the PKI engine enabled and tuned with vault secrets tune -max-lease-ttl, a root CA from vault write pki/root/generate/internal, URLs via vault write pki/config/urls and short-lived X.509 certificates issued from a role with allowed_domains, allow_subdomains and max_ttl through vault write pki/issue; the Kubernetes auth method that lets a pod prove identity with its own service-account token via vault write auth/kubernetes/config and a role binding bound_service_account_names and bound_service_account_namespaces to a policy so no bootstrap secret is provisioned; narrow deny-by-default policies granting read on exactly one creds path; and a production checklist covering root rotation, dynamic over static, realistic TTLs, platform-identity auth and audit devices.

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PulumiInfrastructure as CodeIaC

Pulumi for Data Infrastructure — Type-Safe IaC, Multi-Cloud, and Developer-First Provisioning

A practical guide to using Pulumi as infrastructure as code in a real programming language rather than a bespoke configuration DSL: the three-noun model of a program that describes how your cloud is composed, a project directory carrying that program plus metadata, and a stack as an isolated configurable instance so dev, staging and production are stacks of one project; the required Pulumi.yaml keys name and runtime with runtime set to nodejs, python, go, dotnet, java, yaml or bun plus optional description, main and a backend url, and per-stack settings in sibling Pulumi.<stack>.yaml files written by pulumi config rather than by hand; the everyday loop of pulumi new to scaffold from a template, pulumi preview to see the diff without changing anything, pulumi up to create or update and pulumi destroy to tear down; writing the program in Python from __main__.py by importing pulumi and a provider SDK like pulumi_aws, reading per-stack values through pulumi.Config and exporting values with pulumi.export; the Input and Output type system where a property such as bucket.id is a future value that also carries the dependency graph, why you transform it with .apply and combine several with pulumi.Output.all rather than concatenating strings, and how passing one resource's output as another's input lets Pulumi infer ordering and maximize safe parallelism; packaging repeated resource groups by subclassing pulumi.ComponentResource with a package:module:Type token, ResourceOptions(parent=self) child resources and register_outputs; where state lives across the managed Pulumi Cloud backend and DIY object stores selected by pulumi login over s3://, gs://, azblob:// and file:// with checkpoint history in .pulumi/history and the reassurance that state never contains cloud credentials; encrypting sensitive config with pulumi config set --secret and choosing among the default, passphrase, awskms, gcpkms, azurekeyvault and hashivault providers via pulumi stack init --secrets-provider; and the Automation API shipped as pulumi.automation for driving up, preview, refresh and destroy from inline programs in CI/CD, integration tests and custom tools.

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VictoriaMetricsMonitoringTime Series

VictoriaMetrics — High-Performance Metrics Storage, PromQL-Compatible, and Cost Reduction vs Prometheus

A practical guide to running VictoriaMetrics as a Prometheus-compatible monitoring backend and time series database rather than pushing a single Prometheus past the point where memory grows with active series and retention is bounded by one disk: the single-node victoria-metrics binary published as the victoriametrics/victoria-metrics Docker image with no external dependency, listening on port 8428 by default and configured with -storageDataPath and -retentionPeriod which defaults to 1 month and is read in months; the vendor's own prominent-feature claims of up to 7x less RAM and up to 7x less storage than Prometheus, Thanos or Cortex treated as benchmarks to verify rather than laws; ingestion over Prometheus remote_write to /api/v1/write plus the exposition format, InfluxDB line protocol over HTTP, TCP and UDP, Graphite, OpenTSDB, DataDog, OpenTelemetry, CSV and native binary; querying through the familiar /api/v1/query and /api/v1/query_range handlers with the MetricsQL language that is backwards-compatible with PromQL and adds WITH templates, the default operator, keep_metric_names, and if/ifnot while intentionally differing on how rate and increase treat samples before the lookbehind window; scraping with vmagent as a drop-in for Prometheus reading -promscrape.config and fanning out to multiple -remoteWrite.url destinations with on-disk buffering at -remoteWrite.tmpDataPath on port 8429; alerting with vmalert executing Prometheus-format rules against -datasource.url, notifying Alertmanager via -notifier.url and persisting recording rules via -remoteWrite.url on port 8880; the cluster version splitting into vmstorage, vminsert and vmselect on ports 8482, 8480 and 8481 connected by -storageNode with tenant-per-URL multitenancy and -replicationFactor; and cost control through -dedup.minScrapeInterval for HA pairs and Enterprise-only -downsampling.period in offset:interval form.

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KubeflowMLOpsKubernetes

Kubeflow Pipelines — End-to-End MLOps on Kubernetes with Experiments, Artifacts, and Serving

A practical guide to Kubeflow Pipelines and the wider Kubeflow platform for running MLOps on Kubernetes rather than in a notebook that dies on a laptop: the modular platform map of Pipelines, Katib, Notebooks, the Trainer, a Spark operator, a Model Registry, and a central dashboard; authoring components as type-annotated Python functions with the kfp v2 SDK @dsl.component decorator that turns a function into a containerized step, pinning base_image and packages_to_install for reproducibility versus installing dependencies at run time; the sharp line between parameters passed by value and artifacts passed by reference with the Dataset, Model, Metrics, and Artifact types wrapped in dsl.Input and dsl.Output, and the .path, .uri, and .metadata each artifact exposes; logging scalar metrics with Metrics.log_metric and confusion matrices with ClassificationMetrics.log_confusion_matrix after a .tolist() conversion plus log_roc_curve for ROC visualizations; assembling a pipeline with @dsl.pipeline where data dependencies infer the DAG and .after() adds explicit ordering edges; the discipline of compiling to intermediate-representation YAML with compiler.Compiler().compile and submitting runs with the KFP Client via create_run_from_pipeline_package so the versioned YAML is the reproducible contract; hyperparameter tuning with Katib driven by an Experiment custom resource or the KatibClient().tune SDK with search.int and search.double spaces, objective_metric_name parsed from stdout, max_trial_count, and resources_per_trial fanning trials out as pods; serving the trained model as a KServe InferenceService custom resource with modelFormat and storageUri, runtime autoselection, kubectl apply, and V1 versus V2 prediction protocols; and a production checklist covering compile-in-CI, pinned images, right-sized trial resources, artifact lineage, and GPU capacity planning.

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Apache SupersetBusiness IntelligenceData Visualization

Apache Superset in Production — Dashboards, Semantic Layer, Row-Level Security, and Deployment

A practical guide to running Apache Superset, the open-source BI platform built on Flask, React, and SQLAlchemy, as a real production system rather than a demo: getting a working instance up with the project's Docker Compose setup that brings up the web app, a Postgres metadata database, Redis, and the Celery worker and beat pre-wired — cloning the repo with a shallow clone, fetching tags, checking out the 6.0.0 release tag, and logging in at port 8088 with the default admin credentials while SUPERSET_LOAD_EXAMPLES controls example data; the pip-based install and CLI bootstrap with FLASK_APP and SUPERSET_CONFIG_PATH, superset db upgrade, superset fab create-admin, and superset init, plus the SQLALCHEMY_DATABASE_URI metadata database that every clustered node must share and that cannot be SQLite; connecting analytics databases as SQLAlchemy URIs so queries push down to engines like Postgres, ClickHouse, or Trino; the deliberately thin dataset-based semantic layer with physical versus virtual datasets, Jinja-templated SQL, metrics as aggregate expressions and calculated columns as row-level expressions, and the warning not to embed GROUP BY or ORDER BY in a virtual dataset because Superset wraps it as a subquery; SQL Lab as both the SQL IDE and the debugger behind every chart via View query and Run in SQL Lab; Row-Level Security for multi-tenancy with Regular versus Base filters, the group_key that combines rules with OR within a group and AND across groups, Jinja helpers like current_username(), and the critical caveat that RLS does not sandbox raw SQL Lab access; the Admin, Alpha, and Gamma role hierarchy and why superset init reverts edits to built-in roles; scaling with a Celery worker and a single beat scheduler backed by Redis via CeleryConfig, CELERY_CONFIG, and RESULTS_BACKEND; the DATA_CACHE_CONFIG, CACHE_CONFIG, FILTER_STATE_CACHE_CONFIG, and EXPLORE_FORM_DATA_CACHE_CONFIG caching layers with scheduled cache warmup; and a production checklist covering SECRET_KEY, shared metadata, one-beat-many-workers, layered roles, RLS limits, and Kubernetes for production.

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CrossplaneKubernetesPlatform Engineering

Crossplane — Kubernetes-Native Infrastructure Provisioning with Composite Resources and XRDs

A practical guide to Crossplane, the open-source CNCF control-plane framework that turns a Kubernetes cluster into a universal control plane for provisioning and continuously reconciling external infrastructure as ordinary Kubernetes objects: the building blocks — Providers that install managed-resource CRDs for a cloud or SaaS system, Managed Resources that wrap one external resource each and reconcile it forever, Compositions that bundle several managed resources into one higher-level unit, CompositeResourceDefinitions (XRDs) that define your own custom platform API and its schema, and Composite Resources that developers create to get infrastructure; installing the core controllers with the crossplane-stable Helm chart into the crossplane-system namespace, installing a Provider package such as provider-aws-s3 by digest for reproducibility and customising its pod with a DeploymentRuntimeConfig, declaring managed resources directly with the Crossplane v2 namespace-first model where both composite and managed resources are namespaced by default and namespaced provider kinds carry the .m group suffix like rds.aws.m.upbound.io, defining a platform API with an apiextensions.crossplane.io/v2 CompositeResourceDefinition with a scope field defaulting to Namespaced plus Cluster and LegacyCluster modes and served/referenceable versions, wiring a Composition of composition functions with mode Pipeline where each step references an installed Function package like function-patch-and-transform and functions can also be written in Go, Python, KCL, CUE, or Helm-style templates and can request existing resources, the v2 breaking changes that removed native patch-and-transform, ControllerConfig, external secret stores, composite-resource connection details, and the default package registry — checkable with crossplane beta upgrade check — plus GitOps with Argo CD or Flux, the alpha Operation type for day-two workflows, and a production checklist.

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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

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