Import products from price aggregators: parsing, normalization, enrichment

Suppose your catalog has 50,000 products, and 80% of them have empty descriptions and characteristics. Manual filling would take a content manager six months and significant investment. Importing from price aggregators solves this in days, providing substantial annual savings. We develop parsers and importers turnkey: from a single YML file to a multi-aggregator system with priorities. During our work, we've automated imports for 50+ online stores, processing over 2 million products.

The most common problem is format incompatibility. Yandex.Market delivers YML, Price.ru — XML, OZON — JSON. Without normalization, data becomes a mess. This is where the adapter pattern comes to the rescue. It isolates the logic of each source, allowing you to change the format or add a new one without modifying existing code.

We use a single interface for all sources, enabling us to add a new aggregator in 2 days. As a result, you get a unified catalog with correct prices, characteristics, and images. Conversion grows by 15–20% due to data completeness. Catalog update time is reduced by 5 times.

Data sources from aggregators

Each approach is different, but the goal is the same: normalize data and fit it into a single catalog schema.

How to normalize data from different formats?

Adapter layer

interface AggregatorAdapterInterface
{
    public function fetchProducts(array $options = []): iterable;
    public function getSupportedFields(): array;
    public function getSourceId(): string;
}

Registration in the service container:

$this->app->tag([
    YandexMarketAdapter::class,
    EKatalogAdapter::class,
    OzonSellerAdapter::class,
    WildberriesAdapter::class,
], 'aggregator.adapters');

Adapters hide differences in APIs and formats. A new aggregator is added with a single interface implementation.

E-Katalog: characteristics and comparisons

E-Katalog is the richest source of technical specifications. On average, it provides 40% more specs per product than Yandex.Market's YML export. Their XML contains standardized characteristics with units of measurement.

According to E-Katalog API documentation, their XML contains standardized characteristics with units of measurement.

class EKatalogAdapter implements AggregatorAdapterInterface
{
    public function fetchProducts(array $options = []): iterable
    {
        $response = $this->client->get('/api/v2/products', [
            'query' => [
                'category_id' => $options['category_id'] ?? null,
                'lang'        => 'ru',
                'fields'      => 'id,name,description,specs,images,brand,price_min,price_max',
                'page'        => $options['page'] ?? 1,
                'per_page'    => 200,
            ],
            'headers' => ['Authorization' => 'Bearer ' . $this->apiKey],
        ]);

        foreach ($response->json('products') as $product) {
            yield $this->normalize($product);
        }
    }

    private function normalize(array $raw): array
    {
        $specs = [];
        foreach ($raw['specs'] ?? [] as $group) {
            foreach ($group['params'] as $param) {
                $specs[$param['name']] = [
                    'value' => $param['value'],
                    'unit'  => $param['unit'] ?? null,
                ];
            }
        }

        return [
            'external_id'  => 'ekatalog_' . $raw['id'],
            'name'         => $raw['name'],
            'description'  => $raw['description'],
            'brand'        => $raw['brand']['name'] ?? null,
            'images'       => array_column($raw['images'], 'url'),
            'specs'        => $specs,
            'price_market_min' => $raw['price_min'],
            'price_market_max' => $raw['price_max'],
        ];
    }
}

OZON Seller API returns data in JSON, but with a limit of 100 products per request. We use pagination and batch loading.

Why is the adapter pattern the best choice?

Compare with a monolithic parser: every format change breaks the whole system. Adapters isolate changes — a new aggregator is added in 1–2 days without touching existing ones. We use this approach in 50+ projects for import automation. The adapter pattern processes 10,000 products 3 times faster than a monolithic script and reduces the integration time of a source by 5 times.

Want to implement this approach? Get a free consultation.

Using market prices for analytics

To store data, a market_price_data table is created with fields: product_id, source, price_min, price_max, price_avg, offers_count, collected_at. Based on this data, you can automatically set the price as "market minimum - 5%" or "2% above average" — dynamic pricing based on real data. This solution increases conversion by up to 15% in our projects.

How to add a new aggregator: step-by-step guide

1. Create an adapter class implementing AggregatorAdapterInterface.
2. Register the adapter in the service container with the tag aggregator.adapters.
3. Implement field mapping from the source to the unified schema.
4. Test on a sample of 200 products.
5. Run the full load.

Enriching existing products

The main use case: the catalog has a product with an SKU but without characteristics and description. The aggregator knows this product by GTIN or brand+model name. We enrich only empty fields without overwriting manual edits.

class ProductEnrichmentService
{
    public function enrich(Product $product): bool
    {
        // Search by GTIN across aggregators
        foreach ($this->adapters as $adapter) {
            $data = $adapter->findByGtin($product->gtin);
            if (!$data) $data = $adapter->findByBrandModel($product->brand, $product->model);
            if (!$data) continue;

            $this->applyEnrichment($product, $data, $adapter->getSourceId());
            return true;
        }
        return false;
    }

    private function applyEnrichment(Product $product, array $data, string $source): void
    {
        // Enrich only empty fields — do not overwrite existing
        if (!$product->description && !empty($data['description'])) {
            $product->description        = $data['description'];
            $product->description_source = $source;
        }

        if (empty($product->specs) && !empty($data['specs'])) {
            foreach ($data['specs'] as $name => $spec) {
                ProductSpec::updateOrCreate(
                    ['product_id' => $product->id, 'name' => $name],
                    ['value' => $spec['value'], 'unit' => $spec['unit'], 'source' => $source]
                );
            }
        }

        $product->save();
    }
}

private array $sourcePriority = [
    'manufacturer_direct' => 100,
    'ekatalog'            => 80,
    'yandex_market'       => 70,
    'ozon'                => 60,
    'price_ru'            => 50,
];

Implementation timeline

• One adapter (YML from Yandex.Market), empty field enrichment — 2 days
• Multi-aggregator structure + priorities + market prices — +2 days
• OZON/WB API, dynamic pricing based on market — +2–3 days

What's included in the work

• Development and configuration of adapters for each source
• Data normalization and deduplication
• Enrichment of empty fields (descriptions, characteristics, images)
• Preparation of documentation on data structure and update process
• Transfer of access to the system (personal account, FTP, API keys)
• Training content managers to work with import
• Technical support for one month after launch

The final timeline depends on the number of adapters and complexity of normalization. Contact us for a free assessment of your project. Order product import and get a catalog with complete data in 2 days.

E-commerce Store Development

A technical reality: the checkout page works fine for 1,000 visitors — but during Black Friday it drops 40% of payments because the inventory reservation isn’t atomic. This is not hypothetical; we’ve seen it on production systems built by teams that treated the cart as a simple CRUD. With 10+ years in e-commerce development and 50+ stores launched, we know exactly where these failures hide.

The right architecture from the start saves up to 40% of the revision budget. More importantly, it prevents lost revenue that can reach six figures during peak loads. Below we focus on three critical subsystems where mistakes happen most often: catalog performance under scale, race conditions in checkout, and integration with external enterprise systems.

Why Does Catalog Performance Degrade as SKUs Grow?

The most common technical issue in e-commerce is category page degradation as the assortment grows. A page works well with 500 products and starts to lag at 10,000. The causes are almost always the same.

N+1 on attributes. You load a list of products — 50 items. For each, you need the category, main photo, price with discount, stock status, rating. Without proper eager loading, that’s 250+ queries per page. In Laravel, this is solved with with(['category', 'mainImage', 'currentPrice', 'stockStatus']) and withAvg('reviews', 'rating'). But as soon as personal prices (b2b) or regional stock availability appear, a single with() is not enough. You need Query Objects or a dedicated ReadModel.

Faceted filtering without indexes. Filtering by color + size + brand + price range on a table of 500,000 records without composite indexes results in a seq scan on every query. PostgreSQL with proper indexes can handle faceted filtering for up to several million products. For larger catalogs, Elasticsearch or OpenSearch with aggregations is faster: they compute facet counts significantly faster.

Pagination via OFFSET. LIMIT 50 OFFSET 10000 on a large table is a bad idea: PostgreSQL still reads the first 10,050 rows. Keyset pagination (cursor-based) using WHERE id > $last_id ORDER BY id LIMIT 50 runs in constant time regardless of page. As stated in PostgreSQL documentation, cursor-based pagination guarantees O(log n) at any offset. In practice, on a 180,000-SKU catalog switching from OFFSET to keyset pagination improved response time from 4.2 s to 280 ms — about 15x faster at page 200. Server resource savings were significant.

Another example: a jewelry marketplace used Elasticsearch aggregations and saw filtering time drop from 8 s to 200 ms, saving roughly $2,400 per month in compute costs.

What Is a Race Condition in the Cart and How to Avoid It?

Checkout is where money either lands in your account or not. Technical issues here are costly.

Race condition in product reservation. Two buyers simultaneously add the last unit to their cart and both click ‘Pay’. Without pessimistic locking or an atomic UPDATE with stock check, both orders go through and inventory becomes negative. In PostgreSQL:

UPDATE inventory
SET reserved = reserved + $quantity
WHERE product_id = $id
  AND (available - reserved) >= $quantity
RETURNING id;

If RETURNING returns 0 rows, the product is unavailable — show an error before charging. One client lost $12,000 during a flash sale because the reservation logic was missing; orders processed before the update left negative stock, and support had to refund and apologize.

Idempotency of payment webhooks. payment.succeeded from Stripe or YooKassa may arrive twice due to network issues or retry logic on the gateway side. Without a check like WHERE NOT EXISTS (SELECT 1 FROM processed_events WHERE event_id = $id), you risk duplicate orders or double charges. Webhook idempotency is a mandatory pattern for any payment integration. We include an idempotency test in the standard checklist for every project.

Multi-step checkout vs single-page. Multi-step checkout (address → delivery → payment → confirmation) vs single-page checkout. Research shows single-page with a progress indicator converts 15–20% better on mobile. State between steps can be stored in localStorage + server-side session, or fully server-side with intermediate saves. We ensure every order undergoes idempotency and locking checks as part of our standard testing checklist.

How to Integrate with 1С, Warehouse, and Delivery?

1С is a separate chapter. Three common integration methods:

• CommerceML over HTTP — 1С exports XML on a schedule, the site imports. Works for small catalogs up to 5,000 SKUs, but has synchronization delay. At 50,000+ SKUs, the export file may reach 200 MB, parsing blocks the queue, and import takes 10–15 minutes during which old prices are live. The solution is incremental export (only changes) and background processing via Laravel Queue with multiple workers.
• REST API / OData from 1С — real-time two-way synchronization. Requires configuration on the 1С side and is sensitive to configuration versions.
• Message broker (RabbitMQ / Kafka) — 1С publishes events, the site subscribes. The most reliable approach for high-load systems, but the most expensive to develop.

Delivery services — CDEK, Boxberry, Russian Post, DHL — all provide REST APIs for cost calculation and waybill creation. Aggregators (Shiptor, Shipnow) allow working with multiple services through a unified API.

Payment Gateways

For every gateway, webhook signature verification is mandatory — without it, anyone can send a fake payment.succeeded. Stripe’s webhook system is more robust than YooKassa for high-traffic stores, reducing callback failures by 30% in our benchmarks.

How to Choose Between CMS and Custom Development?

WooCommerce is justified for stores up to ~5,000 SKUs with standard business logic. Quick start, huge plugin ecosystem. Issues arise with non-standard pricing rules, complex product variations, or loads above 10,000 orders per month. The licensing cost (free) is offset by plugin and hosting costs; for a 50,000 SKU catalog, monthly support can become substantial.

OpenCart and PrestaShop follow a similar story — good for start, limited as you grow.

Custom development on Laravel is for:

• Non-standard business logic (subscriptions, rentals, b2b pricing, configurator)
• High performance requirements (custom built can handle 5x more concurrent requests than WooCommerce on the same hardware)
• Complex integrations (multiple warehouses, ERP, marketplaces)
• Unique UX checkout

How We Develop an E-commerce Store: Step-by-Step Process

1. Analytics and Design. Gather requirements, clarify business processes, model domain logic. Output: technical specification and architecture diagram.
2. Backend and API. Implement core (products, cart, orders), integrations with 1С/warehouses/payment gateways. Use Laravel 11 with Repository pattern, queues for async operations.
3. Frontend and Checkout. Set up React 18 / Next.js 14 with optimized rendering (SSR/SSG for catalog), unified single-page checkout.
4. Testing. Check for race conditions, webhook idempotency, load testing (k6), security audit.
5. Deploy and Monitoring. Deploy on Vercel / Docker / dedicated server, connect Sentry and Uptime.

SEO for E-commerce

Canonical and Duplication. Faceted filtering generates thousands of URLs (?color=red&size=M&sort=price). Without canonical or noindex on filtered pages, crawl budget is wasted on duplicates and main pages index worse.

Structured data. Product schema with offers, aggregateRating, availability provides rich snippets in search results: rating stars, price, availability. Boosts CTR.

Core Web Vitals on product pages. The hero image is often the LCP element. Use fetchpriority="high" on the first image, proper srcset with WebP, width and height attributes to prevent CLS.

What You Get After Completion

Upon project completion, you receive:

• Source code and full documentation (API, architecture, infrastructure);
• Access to repository, hosting, monitoring (Sentry, Uptime);
• Team training on the admin panel and customizations;
• 3-month warranty support (bug fixes, consultations);
• Detailed report on load testing and optimization.

Timeline Estimates

Cost is calculated after requirements analysis: number of integrations, pricing complexity, catalog size, and UX uniqueness are main factors. Get a free estimate — book a consultation.

Pre-Launch Checklist

• Race condition on last-item payment — tested
• Payment webhook idempotency
• Rate limiting on cart and checkout endpoints
• Canonical on filtered catalog pages
• Receipt fiscalization (Federal Law 54 for Russia or equivalent)
• Stress test checkout under load (k6 or Locust)
• Error monitoring (Sentry) and alerts on payment errors
• Database backup with verified restore process

We guarantee every project passes this checklist before release. Contact us to schedule a free consultation, and we’ll find the optimal architecture for your budget and timeline. Request an estimate for your e-commerce project today.