What is a Real-Time Data Lake?

A data lake is a centralized data repository where structured, semi-structured, and unstructured data from a variety of sources can be stored in their raw format. Data lakes help eliminate data silos by acting as a single landing zone for data from multiple sources. But what’s the difference between a traditional data lake and a real-time data lake?

Some traditional data lakes use batch processing, which involves processing and analyzing a collection of data that has been stored over a specific timeframe. For example, payroll and billing systems that are handled on a weekly or monthly basis might use batch processing.

To contrast, real-time or live data streaming occurs while data is in motion through a system. This results in the immediate analysis and reporting of ongoing events. For example, scenarios like fraud detection or intrusion detection leverage real-time processing. Streaming data processing ensures that information is analyzed and actions are swiftly taken within a brief timeframe, closely approximating real-time conditions.

Real-time data lakes store real-time data as soon as it is generated without making assumptions about the data’s structure or type. In doing so, real-time data lakes give organizations flexibility to adapt and change their data strategies to current, in-the-moment business scenarios and conditions.

Understanding data lakes at a glance

Today’s data lakes are data storage repositories that centralize, organize, and protect high volumes of structured, semi-structured, and unstructured data across multiple sources. Unlike data warehouses that follow a schema-on-write approach (data is structured as it enters the warehouse), data lakes follow a schema-on-read approach (where data can be structured at query-time based on the user’s needs).

Key components of a data lake architecture include:

• Data Sources: Applications that generate enterprise data.
• Data Ingest Layer: Software that captures enterprise data and moves it into the storage layer.
• Data Storage Layer: Software storage repository for raw data — whether it’s structured, semi-structured, or unstructured data.
• Catalog/Index Layer: Software that catalogs or indexes the data to make it searchable and accessible for transformation and analysis.
• Client Layer: Software that enables data transformation, analysis, visualization, and insight development.

Beyond these five core components, a data lake usually includes a way to govern data, such as metadata management, role-based access control (RBAC), or data lineage.

The pros and cons of traditional batch processing data lakes

While batch processing certainly is efficient, it comes with its share of challenges for organizations that want to take advantage of real-time data processing capabilities. Here are some of the core benefits and challenges of batch processing data lakes.

Benefits of batch processing

• Cost-Effective Data Processing: Batch processing allows for efficient utilization of resources by processing data in larger chunks at scheduled intervals, reducing the overall cost of data processing.
• Scalability: Batch processing systems can be scaled vertically (increasing resources of a single node) or horizontally (adding more nodes to the cluster), making it easier to handle growing data volumes.
• Simplicity: Batch processing is relatively straightforward to implement and manage, as data is processed in fixed intervals without the need for complex real-time data handling.
• Data Integrity and Consistency: Batch processing ensures consistent results, as data is processed in a sequential manner, reducing the likelihood of concurrency issues.
• Data Transformation and Enrichment: Batch processing allows for extensive data transformation, cleaning, and enrichment before loading into the data lake, improving data quality for downstream analytics.

Challenges of batch processing

• Latency: Batch processing introduces latency as data is processed in fixed intervals. This delay can impact timely decision-making and real-time insights.
• Outdated Insights: Since data is processed in batches, insights are based on historical data rather than the most recent information, limiting the ability to respond to rapidly changing situations.
• Limited Real-Time Analytics: Batch processing is ill-suited for applications requiring real-time analytics, such as fraud detection or monitoring critical infrastructure.
• Complex Data Pipelines: Developing and maintaining complex ETL (Extract, Transform, Load) pipelines for batch processing can be time-consuming and resource-intensive.
• Resource Constraints: During batch processing, resources may be fully utilized, causing performance bottlenecks and potentially affecting other processes running on the same infrastructure.
• Data Quality Challenges: The delay between data collection and processing can lead to discovering data quality issues late in the process, making it harder to correct errors.

Organizations must weigh these pros and cons, as well as their data lake use cases, to determine whether real-time or batch processing is right for their needs.

The emergence of live data streaming

Real-time data analytics matters more and more as organizations demand timely insights.

In many industries, making decisions based on historical data alone is no longer sufficient. Real-time insights enable organizations to make informed decisions as events unfold, leading to more accurate and effective outcomes. For example, some industries such as fraud detection and security demand real-time insights to prevent financial losses and protect sensitive data.

Other organizations turn to real-time data to increase operational efficiency and enhance customer experience. Real-time insights help optimize operations by identifying bottlenecks, inefficiencies, or issues instantly, enabling proactive adjustments to processes and resources. For customer experiences like e-commerce recommendations and customer support, real-time insights can provide personalized and contextualized interactions that improve customer satisfaction.

The benefits of continuous processing

Now that we know more about the value of real-time processing, let’s look into the key components of a streaming data architecture.

Data ingestion: Continuously processed data streams

Real-time data lakes enable data capture from multiple sources including IoT devices, social media, applications, sensors, and more. Data streams are ingested and continuously processed as they are generated, ensuring a constant flow of fresh information. An event-driven architecture is at the core, meaning data is ingested based on events or triggers, allowing for immediate processing and analysis as events occur. This architecture accommodates high-velocity data influx by distributing the load across multiple nodes and enabling parallel processing.

Stream processing: Transforming and analyzing data on the fly

Real-time data lakes use micro-batch processing techniques to transform and analyze data in small increments, reducing latency. They also leverage complex event processing to detect patterns, correlations, and anomalies in real-time data streams. Data is aggregated and summarized on the fly, allowing for instant calculations and metrics generation. In addition, data from various sources can be enriched, joined, and correlated in real-time to provide comprehensive insights.

Storage and query: Enabling streaming analytics

Some teams leverage NoSQL databases to power analytics on their real-time data lakes. These solutions provide flexible schema designs to accommodate evolving data structures. Other technologies like ChaosSearch enable streaming analytics by transforming existing cloud object data stores like Amazon S3 into a data lake, giving teams the ability to cost-effectively store and analyze data in AWS with multimodal data access (SQL, Search, and ML), no unnecessary data movement, no fragile and time-consuming ETL pipelines, and no limits on data retention.

For example, AWS customers can use Kinesis Data Streams to ingest logs from multiple sources and deliver them to Amazon S3 cloud object storage at scale. Once the data lands in S3, ChaosSearch can index the data with proprietary indexing technology and up to 20x file compression, making the data fully searchable.

From there, ChaosSearch users can trigger the indexing process after creating an object group in S3, or take advantage of ChaosSearch Live Indexing capabilities to monitor Amazon S3 for new object creation events and automatically index the newly-created log data to make it available for querying. Users can then create virtual views to analyze and visualize data in different ways — without data movement or changes to the underlying data.

Turning on real-time processing

Real-time data lakes are ideal for teams that need to analyze data in real-time across multiple sources, unlike their batch processing counterparts. Fortunately, through solutions like ChaosSearch, teams can transform their existing cloud object storage into a real-time data lake, without data movement or complex data transformation. As many teams combat the high costs of real-time analytic tools or observability solutions, they should consider alternatives that leverage their existing infrastructure, combined with streaming analytics capabilities.