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According to Gartner, poor data quality and inefficient processing cost the organizations an average of $12.9 million annually. What is important is that these losses are mainly related not to storage. Instead, these millions are spent on the compute-heavy efforts to clean and reconcile data silos that should have been integrated at the ingestion layer. Apart from this, low quality of data is a top barrier to scaling AI development and digital transformation projects. It results in increasing complexity of data ecosystems and decision-making paralysis.
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These bottlenecks prevent data-driven systems from delivering value, turning them into expensive digital archives. Сorrectly chosen techniques are key to unlocking the full potential of the data that your organization possesses.
This article explores fundamental big data processing techniques and best practices that organizations can leverage to reduce operational inefficiencies and extract actionable insights from their data.
Parameters of Quality in Big Data Processing
Poor engineering is not the only reason data platforms fail. Often, these platforms fail simply because performance standards are vague or undefined. Performance can’t be judged by feeling alone. It should be characterized by a set of rigid, measurable benchmarks that align infrastructure performance with business survival.
If your data is mostly accurate but your platform needs 24 hours to process it, this data is often useless for operational decision-making. Meanwhile, if it is real-time but costs more to process than the revenue it generates, it is a financial leak.
First of all, you should define success based on the following dimensions:
- Latency and freshness (delta between an event occurring and that event being queryable);
- accuracy (percentage of records that pass schema validation and checksums);
- cost efficiency (cost per query or cost per GB processed);
- availability (system uptime and data accessibility during peak ingestion windows).
These outcomes translate into concrete operational targets.
Based on our experience in working with big data, we have formulated clear practical recommendations that will help you enhance your data processing workflows.
Document Your SLIs
A practical starting point is to document service level indicators (SLIs) for each critical pipeline. These are the raw metrics that indicate system health. You can’t optimize what you don’t measure.
- End-to-end latency (time from source emission to warehouse availability);
- throughput (total records or bytes processed per second);
- error rate (percentage of malformed records or failed pipeline runs);
- data completeness (ratio of expected vs. actual records received from a source).
Set SLOs Tied to Business Use Cases
An SLO (service level objective) is the target your SLIs must hit. They should be based on your business needs and goals. Here’s what they may look like in practice.
- Fraud detection can be set for < 5-minute latency. If latency hits 6 minutes, the system is failing the business.
- Sales reconciliation can be targeted at < 2-hour completion for daily rollups.
- Executive dashboards should ensure 99.9% data availability, for example, by 8:00 AM EST daily.
Establish Data Contracts
The most dangerous problem in big data is the silent failure, when an upstream team changes a database schema and breaks the downstream pipeline.
To avoid this, you need to implement field-level data contracts.
Every critical dataset must have a defined producer, schema versioning, ingestion cadence, and explicit rules on allowed nulls. If the incoming data violates the contract, the pipeline triggers an alert before the error hits your clean data layer.
Metrics that Truly Matter
Sometimes, teams think that the more parameters they monitor, the better they will know their systems. In reality, it is enough to track a rather small set of operational metrics to get a good understanding of real system health.
Big Data Processing Techniques: Batch, Micro-Batch, and Streaming
Modern data platforms rely on three core processing paradigms. Each of them is optimized for different latency, scalability, and consistency requirements. To build reliable data pipelines that balance speed with cost and complexity, you should understand where they fit and how they influence system architecture.
Choosing real-time processing when you only need a daily option is an expensive architectural mistake. Forcing batch processes onto event-driven data creates a visibility lag that can cost thousands in missed opportunities.
Batch Processing: High Throughput, High Latency
Batch processing is the architectural baseline for workloads with high data gravity. It operates on large volumes of data collected over a defined period, such as hourly, daily, or monthly intervals. And then, it processes it as a single, massive unit.
It is the most cost-efficient way to use CPU/RAM because it maximizes resource utilization.
Best for:
Historical reprocessing. When you need to re-calculate three years of financial metrics due to a logic change, you can use a batch engine (like Spark) to ensure 100% accuracy across billions of rows.
Micro-batch Processing: Operational Middle Ground
This approach sits between batch and real-time systems. Micro-batching breaks the continuous data stream into tiny chunks (for example, every 2 to 30 seconds) and processes them.
It simplifies error handling. If a micro-batch fails, you only need to re-process that specific 10-second window rather than a complex stateful stream.
Best for:
Log analytics, monitoring, and near-real-time dashboards. If you monitor app logs for error spikes, a 10-second delay is acceptable. It provides near-real-time visibility without the overhead of true continuous processing.
Continuous Streaming: Millisecond Decisions
It processes events one at a time as they arrive. This enables sub-second latency and immediate system responses. Continuous streaming requires advanced state management and more careful design around fault tolerance.
Best for:
IoT telemetry, fraud detection, and operational automation. If a sensor indicates a turbine is overheating, or a credit card is being swiped in two countries simultaneously, a 10-second micro-batch delay is already too late.
The table below contains the key differences between these three big data processing techniques.
Lambda and Kappa Architectures
These paradigms map to broader architectural patterns.
The Lambda architecture runs two parallel paths. They are a speed layer (streaming) for immediate insights and a batch layer for the source of truth.
In this case, you are forced to maintain two separate codebases. One should be for Spark/SQL (batch) and one for Flink/Java (streaming). This doubles your integration debt and leads to logic drift, where the speed layer and batch layer produce slightly different results for the same query.
The Kappa architecture treats everything as a stream. If you need to re-process historical data, you shouldn’t use a separate batch job. You can simply rewind the message broker (Kafka) and replay the events through your streaming logic.
The key benefit here is a single codebase, which ensures total consistency. It requires a robust broker with high retention, but it eliminates the friction of maintaining dual architectures.
Data Ingestion and Integration
Modern platforms typically ingest data from a mix of operational databases and external systems. Each source requires a different integration approach.
Common sources include change data capture (CDC) from:
- OLTP databases (PostgreSQL, MySQL, SQL Server);
- application events (high-velocity JSON payloads);
- APIs (third-party data like Stripe, Salesforce, or Zendesk);
- Files (Bulk CSV or Parquet uploads from legacy systems or external partners).
The modern standard is to funnel these sources into a message broker like Apache Kafka or Pulsar. If your warehouse goes offline for maintenance, the broker holds the data until the system recovers.
Change data capture has become the preferred pattern for integrating transactional systems with analytical platforms without impacting production performance. Instead of querying the database directly, you can use tools like Debezium to tail the database's transaction logs.
Key CDC advantages:
- Zero load on production. Production performance is unaffected thanks to reading the log file on disk instead of hitting the compute layer.
- Capture deletes. Standard API polling misses deleted records. CDC captures the DELETE event in the log and ensures your data lake is a perfect mirror of reality.
- Real-time synchronization. Data moves from your database to your lakehouse in milliseconds.
However, CDC also introduces some complexities.
Ordering Challenge
In a distributed system, packet A can arrive after packet B. If a user changes their email and then deletes their account, receiving those events in the wrong order will result in a ghost account in your analytics that shouldn't exist.
To address such issues, you can use key-based partitioning in Kafka. By applying the UserID as the partition key, you can guarantee that every event for that specific user is processed in strict chronological order by the same worker.
Deduplication
When a producer doesn't receive an ACK from Kafka, it will re-send the data, creating a duplicate record.
The most helpful recommendation here will be to implement idempotent consumers. Every record should have a Universally Unique Identifier (UUID) from the source. Your ingestion layer must check this ID against the available cache to ensure a single event doesn’t get counted twice in your final metrics.
Compute Engines and When to Use Them
The computer engine is the most expensive line item in your data budget. It determines how efficiently data can be processed and queried. And if you choose the wrong one, financial losses will be only a part of all the negative consequences that you may face. The application of the inappropriate compute engine results in logic debt, which will lead to the necessity of rewriting your entire transformation layer.
Modern platforms rarely rely on a single engine. Instead, they combine multiple engines optimized for different workloads, balancing latency, scale, and developer productivity.
It’s crucial to choose engines based on your latency and throughput requirements, and your team’s existing language expertise. Let’s consider the most commonly used options.
Apache Spark
Spark is the industry standard for batch and micro-batch big data processing techniques. It applies the same operation across massive datasets and excels at multi-stage data extraction, transformation, and loading (ETL), as well as data parallelism.
Spark’s streaming is actually high-speed micro-batching. If you need latency below 100ms, Spark isn't your tool.
Apache Flink and Beam
If your business depends on event-at-a-time processing, Flink is a strong choice. Unlike Spark, it doesn't wait for a batch to fill. It processes data as it arrives.
It eliminates latency spikes in real-time applications. That’s why Flink is often used in fraud detection and IoT telemetry.
Apache Beam can be applied when you want to write your logic once and have the flexibility to switch runners without a full rewrite.
Trino and Presto
These are MPP (massively parallel processing) engines designed for interactive, ad-hoc SQL. They don’t store data. They query it where it resides.
You can query S3, MySQL, and Kafka in a single SQL join without moving data first.
Such engines are optimized for speed, not fault tolerance. If a node fails during a massive 2-hour join, the query usually fails entirely.
They are well-suited for internal BI dashboards, data exploration, and data lakehouse querying.
Dask and Ray
For machine learning and deep learning projects, the use of Spark is often a friction point. Meanwhile, Dask and Ray scale Python code natively.
Dask is often applied for scaling the PyData ecosystem from a laptop to a cluster.
At the same time, Ray is optimized for distributed AI and ML. It is the engine of choice for training and serving large-scale models.
Kafka Streams
Kafka Streams is a purpose-built tool for event processing directly on streaming data.
It helps fix operational complexity. Thanks to this, there is no need to manage a Spark or Flink cluster just to do simple stream enrichment.
It is appropriate for lightweight event-driven microservices, real-time data cleaning, as well as simple stateful aggregations.
Wrapping Up
As real-time and AI-driven use cases expand, the importance of selecting the right processing approach will only increase.
Efficient big data processing techniques are about removing bottlenecks. When choosing the most appropriate one, you shouldn’t just focus on the newest tool. Instead, it’s vital to concentrate on orchestrating your current infrastructure to handle enrollment spikes and seamless processing without crashing the database.
The introduction of advanced analytics and data-driven workflows can’t be powered just by collecting more data. The priority is scaling analytics without sacrificing reliability or control.
Do you need strategic advice or custom digital tools for your big data systems? At Tensorway, we can help you find the right solution. Contact us to book a free consultation with our experts.
