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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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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.

RedpandaApache KafkaEvent Streaming

Redpanda — Kafka-Compatible Streaming Without ZooKeeper and JVM Overhead

A practical guide to Redpanda, the streaming platform that speaks the Apache Kafka API but reimplements it from scratch in C++: the single-binary architecture built on the Seastar thread-per-core framework that pins each application thread to a physical core and communicates by message passing instead of locks, why there is no JVM to garbage-collect or tune and no ZooKeeper or separate KRaft quorum to operate, the Raft consensus algorithm used throughout the platform to coordinate writes and replicate the distributed log so replication factor must be odd, the Seastar custom memory allocator and the choice to bypass the Linux page cache and manage memory and disk I/O directly, sizing around roughly one GB/sec of writes per core and two cores to saturate an NVMe disk, running a cluster with Docker Compose and driving it with the rpk CLI including rpk cluster info, rpk topic create with -p/--partitions and -r/--replicas and -c/--topic-config, rpk topic produce and consume with --num and --offset and --group, pointing existing confluent-kafka clients at the bootstrap address unchanged, Tiered Storage that offloads older log segments to S3, GCS, or Azure Blob via redpanda.remote.write and redpanda.remote.read, the built-in Schema Registry and HTTP proxy and Kafka Connect compatibility, migrating from Apache Kafka with MirrorMaker 2 by cutting consumers over group by group before flipping producers, and a production checklist plus the caveat that the vendor 10x-lower-p99 and 6x-cost-reduction claims should be validated on your own workload.

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Apache HudiLakehouseData Lake

Apache Hudi — Incremental Data Processing, Record-Level Upserts, and CDC for Data Lakes

A practical guide to Apache Hudi, the transactional data lake platform: choosing between Copy-on-Write tables that store only columnar Parquet base files for read-heavy workloads and Merge-on-Read tables that append updates to row-based Avro log files for low-latency writes and CDC, organizing data into versioned file groups and file slices, defining record identity with hoodie.datasource.write.recordkey.field and resolving conflicting versions with an ordering field, the write operations selected via hoodie.datasource.write.operation including the default upsert that tags records via an index lookup so the table never shows duplicates, insert that skips the lookup, bulk_insert for scalable initial loads, soft and hard delete, and insert_overwrite and delete_partition for backfills, indexing with hoodie.index.type across SIMPLE, BLOOM, the GLOBAL_ variants that enforce table-wide key uniqueness at higher lookup cost, BUCKET hashing, and the metadata-table Record Level Index plus bloom-filter, column-stats, expression, and secondary-index partitions for data skipping, the snapshot, read-optimized, and incremental query types with time travel and the _hoodie_commit_time metadata column, Spark SQL DDL with USING hudi and TBLPROPERTIES type cow or mor, primaryKey, and orderingFields plus MERGE INTO upserts, the compaction, cleaning, and clustering table services with inline versus async compaction controlled by hoodie.compact.inline and hoodie.compact.inline.max.delta.commits, and the Hudi 1.0 changes — the LSM tree timeline, non-blocking concurrency control for Flink streaming writers, secondary and expression indexes, and partial updates on Merge-on-Read.

Read more
LangGraphAI AgentsLangChain

LangGraph — Stateful Multi-Agent Workflows with Cycles, Branching, and Human-in-the-Loop

A practical guide to building stateful AI agents with LangGraph, the open-source orchestration library from the LangChain team: installing the langgraph Python package, modeling a workflow as a StateGraph with a TypedDict state and the add_messages reducer attached via Annotated so message history appends instead of overwriting, adding nodes as functions that return partial state updates and wiring them with add_edge plus the START and END sentinel nodes before calling compile() to get a CompiledStateGraph implementing the Runnable interface, building the reason-act loop with add_conditional_edges routing on a function that returns a key mapped to the next node and the prebuilt ToolNode that reads tool_calls from the latest message, guarding cycles with the recursion_limit and GraphRecursionError, durable execution with checkpointers where InMemorySaver suits tests but SqliteSaver and PostgresSaver persist state keyed by a thread_id in the configurable config so runs resume after a crash, human-in-the-loop with interrupt() that pauses a node and surfaces a payload and Command(resume=...) that resumes it while re-executing the node from the top, parallel map-reduce with the Send API returning a list of Send objects from a conditional edge into a field with an operator.add reducer for safe concurrent writes, the create_react_agent prebuilt that assembles the whole loop, streaming with stream_mode values, updates, and messages, composing multiple agents as supervisor and hand-off subgraphs with Command(goto=...), and a production checklist.

Read more
SQLMeshData Transformationdbt

SQLMesh — Model-Based Data Transformation with Audits, Environments, and CI/CD

A practical guide to SQLMesh, the open-source data transformation framework: installing the sqlmesh Python package and scaffolding a project with sqlmesh init duckdb, writing SQL models as a MODEL() metadata block plus a single SELECT parsed semantically by SQLGlot for column-level lineage and dialect transpilation, choosing model kinds including the default VIEW, FULL, INCREMENTAL_BY_TIME_RANGE with a time_column and @start_ds/@end_ds interval macros, INCREMENTAL_BY_UNIQUE_KEY with a unique_key and when_matched, SCD_TYPE_2_BY_TIME and SCD_TYPE_2_BY_COLUMN for slowly changing dimensions, and SEED/EMBEDDED/EXTERNAL/MANAGED, understanding Virtual Data Environments where each model version gets its own physical table and environments are reference collections so a dev environment reuses unchanged prod tables and promotion becomes a near-instant Virtual Update reference swap, the plan/apply workflow that diffs local files against a target environment and requires confirmation before touching the warehouse, breaking versus non-breaking change categorization on directly modified models with downstream impact inferred from lineage and the conservative category winning conflicts, forward-only plans that reuse physical tables for tables too large to rebuild with --effective-from and --allow-destructive-model, audits as zero-row SQL quality gates with built-ins like not_null, unique_values, accepted_values, number_of_rows, and forall plus custom AUDIT() blocks using @this_model that are blocking by default, Python models with the @model decorator, a required columns schema, and an execute function returning a DataFrame, shipping changes with the GitHub Actions CI/CD bot via sqlmesh_cicd github run-all that builds PR environments and deploys on approval with configurable merge_method and enable_deploy_command, and scheduling data processing with sqlmesh run on cron or Apache Airflow separately from code deploys, plus dbt project compatibility and a production checklist.

Read more
AirbyteData IntegrationELT

Airbyte in Production — ELT Pipelines, Custom Connectors, and dbt Transformations

A practical guide to running Airbyte Open Source for data movement: how the ELT model extracts from a large connector catalog and loads raw into the warehouse before transforming, deploying a local instance with the abctl command-line installer and Kind-in-Docker and a production instance with Helm from the airbytehq/charts repo with externalized S3 log/state storage and managed PostgreSQL metadata, configuring connections with sources, destinations, streams, and sync modes including Full Refresh Overwrite/Append and Incremental Append and Append plus Dedup with cursor fields and log-based CDC, understanding Destinations V2 Typing and Deduping with a raw airbyte_internal table holding the _airbyte_data JSON blob and a typed final table plus per-row _airbyte_meta error capture and the newer Direct-Load direction, why Airbyte deprecated built-in Custom Normalization and basic normalization so transformations now belong to dbt run downstream against the final tables, orchestrating sync-then-transform with Apache Airflow via the apache-airflow-providers-airbyte AirbyteTriggerSyncOperator and AirbyteJobSensor so dbt only runs on complete data, scripting lightweight pipelines with the PyAirbyte airbyte Python library using get_source, select_streams, a swappable DuckDB/Postgres/Snowflake/BigQuery cache, and to_pandas, and building custom connectors across three tiers — the low-code Connector Builder UI, the declarative YAML low-code CDK, and the airbyte-cdk Python package — plus a production checklist covering externalized state, version pinning, incremental syncs, downstream dbt ownership, orchestrated sequencing, secret management, and sync health monitoring.

Read more
KarpenterKubernetesAutoscaling

Karpenter for Kubernetes Autoscaling — Node Provisioning, Spot Instances, and Cost Optimization

A practical guide to Karpenter on AWS EKS: how the just-in-time node autoscaler differs from the Cluster Autoscaler and managed node groups by provisioning right-sized instances directly through the EC2 API instead of scaling fixed node groups, installing Karpenter v1.x from the oci://public.ecr.aws/karpenter/karpenter Helm chart into kube-system with settings.clusterName and settings.interruptionQueue, defining NodePools with the karpenter.sh/v1 API including karpenter.sh/capacity-type requirements for spot, on-demand, and reserved capacity, instance-category and instance-generation constraints, aggregate cpu and memory limits, weight for pool ordering, and expireAfter for node lifetime, configuring EC2NodeClass with the karpenter.k8s.aws/v1 API for amiFamily and amiSelectorTerms alias-based AMI selection, tag-based subnetSelectorTerms and securityGroupSelectorTerms discovery, node role, and blockDeviceMappings, understanding the three consolidation mechanisms empty, multi-node, and single-node under consolidationPolicy WhenEmptyOrUnderutilized versus WhenEmpty with consolidateAfter cooldown, drift detection that rolls nodes onto freshly published AMIs, interruption handling through the SQS interruption queue with the two-minute Spot reclamation notice, spot-first NodePools with on-demand fallback for stateful workloads, disruption budgets that rate-limit voluntary disruption by percentage, reason, and cron schedule, IMDSv2 hardening enabled by default since v1, and a 10-point production checklist covering controller placement, NodePool limits, wide instance selection, PodDisruptionBudgets, AMI drift, and safe v1beta1-to-v1 upgrades.

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

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hello@datasops.com

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