Source: https://daphnislabs.com/services/big-data

# Big Data Consulting & Development

Plan and build distributed data processing for large event streams, historical datasets and analytics workloads that exceed a single-system workflow.

[Discuss your project](https://daphnislabs.com/contact?service=big-data) [Try the example](https://daphnislabs.com/services/big-data#worked-example)

Orders Events Exports

**Partition A**

**Partition B**

**Late arrivals**

**Curated analytics tables**

Schema checks Deduplication Lineage

## Tools & technologies

-   Spark
-   Kafka
-   Object storage
-   SQL warehouse

## Workloads that need distributed processing.

### Event pipelines

Collect and process high-volume events with replay and deduplication.

### Data platforms

Organize lake or warehouse storage around ownership, access and query patterns.

### Analytics modernization

Replace slow batch jobs with partitioned processing and measurable data freshness.

## Pipelines, contracts and operating evidence.

-   **Data-platform architecture**
-   **Ingestion and transformation jobs**

-   **Quality and reconciliation checks**
-   **Capacity and operating-cost model**

## Reconcile late order events

Capability Example

Order events arrive out of sequence, with some duplicates from retries.

### Land

### Process

### Reconcile

Illustrative data. Nothing is sent to an external system.

Example workspace 1 / 3

### Incoming events

```
order_id: 104
event_time: 09:10
received_at: 09:18
```

Keep immutable source events with event time and ingestion time.

### Partition job

```
partition: business_date
deduplicate: event_id
late events: separate window
```

Deduplicate by event ID and recompute affected partitions.

### Published aggregate

```
daily_orders: reconciled
late adjustment: recorded
source partition: traceable
```

Compare aggregate totals with the transactional source.

A refreshed daily order table with traceable corrections and a recorded freshness timestamp.

Follow the record through the next step

## Volume is only one sizing decision.

### Volume and velocity

Size the design using actual event rates, retention and query demand.

### Ownership

Assign data owners and access rules before combining operational sources.

## Questions before we start.

### Do we need distributed processing?

Not automatically. Consulting starts by measuring the bottleneck; a database or scheduled job may be sufficient.

### Can you migrate an existing Hadoop or warehouse workload?

Yes. Inventory jobs, dependencies and reconciliation rules, then move bounded workloads with a rollback path.

[Data Engineering](https://daphnislabs.com/services/data-engineering) [Predictive Analytics Services](https://daphnislabs.com/services/predictive-analytics) [Machine Learning Development](https://daphnislabs.com/services/machine-learning)

## Bring your starting point.

Bring source volumes, current processing times and the reports or decisions waiting on that data.

[Discuss your project](https://daphnislabs.com/contact?service=big-data) [Explore related services](https://daphnislabs.com/services/data-engineering)