The 6 Essentials for Real-Time Data Streaming Architecture

Harnessing robust cloud-based applications can help companies increase revenues by more than 30% yearly. To reach this pot of gold, 40% of businesses plan to pick up the pace of their cloud implementations and follow in the footsteps of popular apps like Uber, Netflix, and Lyft.

The only problem is that there are many hurdles and challenges to overcome before enjoying the benefits of a flexible and scalable cloud infrastructure. The first step in your cloud migration journey is to stream huge volumes of data from existing sources to the cloud. Without the right tools and technologies, data streaming can be time-consuming and costly for your engineers.  

To make migration happen successfully, your data streaming architecture needs to work hard to provide cloud transitions as fast as possible and continually manage a high volume of data.  

What is real-time data streaming?

Real-time data streaming is the constant flow of data produced by multiple sources. It enables you to collect, analyze, and deliver data streams as they generate in real-time. Examples of streaming data include log files produced by users of mobile applications, e-commerce transactions, and telemetry from cloud-based devices.

There are two ways to stream data: batch and real-time. Real-time streaming data is continuously generated, enabling you to use the information for concurrent analysis exactly when you ingest it. Batch processing differs because it receives the data in batches and stores the source until enough data has been collected according to specific parameters. It comes in the form of unending streams of occurrences. This data comes in all sizes, formats, and locations, including on-premise, in the cloud, and a hybrid cloud environment.  

What is data streaming architecture?

Data streaming architecture is a framework of software components that consume and process significant amounts of streaming data from many sources. A streaming data architecture ingests data instantly when you create it, continues it to storage, and could include tools for real-time processing, data manipulation, and predictive analysis. 

Data streams create vast amounts of data, which is primarily semi-structured and needs a lot of pre-processing to be effective and useful. A data streaming architecture contains several components:

Source: There could be tens of thousands of machines or software programs, otherwise called sources, that rapidly and continuously produce large amounts of data. 

Ingestion: Ingestion enables you to capture continuously produced data from thousands of devices reliably and safely.

Storage: Depending on your scale, latency, and processing demands, you can choose a service that will satisfy your storage needs.

Processing: Some processing services require only a few clicks to modify and transport data, allowing you to integrate ML into sophisticated, unique real-time applications.

Analysis: Transmit streaming data to various completely integrated data storage, data warehouses, and analytics services for additional analysis or long-term storage.

What are the use cases of data streaming?

Today’s businesses can’t always rely on batch data processing because it doesn’t allow the visibility they need to monitor data in motion. Data streaming architecture has use cases in almost every sector, from analytics to data science and application integration. This technology is advantageous to every sector that uses big data and can profit from continuous, real-time insights. Business use cases include:

  • Business analytics and performance monitoring
  • Real-time sales campaign analytics
  • Fraud detection
  • Customer behavioral analytics
  • Supply chain and shipping
Real-Time Data Streaming Architecture Equalum

What are the benefits of real-time data streaming?

As long as you can scale with the amount of raw data generated, you can acquire valuable insights on data in transit and use historical data or batch data that has been stored. Here are three main use cases of data streaming:

1. Movement of Real-Time Data

As well as examining data as it is ingested, you can store it for further evaluations by data streams from tens of thousands of endpoints and execute ETL operations on massive quantities of continuous, high-speed data in real-time. 

2. Processing of Event Streams

The most popular use cases involve change data capture (CDC) and communication between a large number of independent microservices for real-time recording, threat monitoring, and event response. 

3. Data Evaluation

Evaluate data as soon as it is generated and allow real-time decisions to improve customer experiences, avoid networking problems, or update your organization in real-time on important business KPIs.

The 6 Essentials for Real-Time Data Streaming Architecture

A flexible streaming architecture simplifies the complexity of conventional data processing architecture into a single self-service product that can convert event streams into data warehouses that are available for analytics. Furthermore, it makes it simpler to keep up with innovation and outperform the competition. Here are the essentials that the best data streaming architecture contains.

1. Scalability 

Thanks to the rise of cloud-based technologies, data streaming architecture is thrust into the spotlight. It needs to be scalable to keep up with increased data volumes, compliance standards, and shifting company needs as businesses adopt cloud tech.

Scalability is especially important when a system malfunctions. The pace of the log data from each source may go from a few KB to MB, maybe even GB. The quantity of raw data proliferates as additional capacity, resources, and servers are added while programs scale. Hence the need for a scalable data streaming architecture. 

2. Fault Tolerance

Fault tolerance is the ability to carry on as normal after a malfunction and enable swift recovery. Your architecture needs advanced systems that transparently recover if a failure occurs. The system’s state must be preserved to ensure no data is lost. 

There are checklists you can follow to improve the fault tolerance of your data streaming architecture, such as preventing a single failure point by using data from various sources and in different forms. You can also maintain high availability and endurance while storing streams of data.

3. Real-Time ETL Tools

Process streaming data is a crucial part of big data architecture in companies with large data volumes. Real-time analytics is made possible by a variety of managed service frameworks that build an end-to-end streaming data pipeline in the cloud. In-memory stream processing has significantly advanced streaming ETL. When you have large datasets that need preprocessing before ingestion into your real-time analytics database, it’s the best option.

For example, Equalum enables real-time, in-memory streaming ETL for replication scenarios, analytics, and BI tools for real-time decision-making. 

4. Storage Options

Real-time data streaming solutions are built to facilitate distributed processing and reduce consumer and producer dependency. Deployment too tightly coupled to one central cluster can choke the autonomy of projects and domains. As a result, the adoption of streaming services and data usage will be limited. Containerization promotes more flexibility and domain independence in a distributed cloud deployment architecture. 

5. Analytics Capabilities 

A streaming data analytics database is made explicitly for analytics, which requires it to quickly prepare enormous data streams for queries after ingestion. Even complex query results should return rapidly. Additionally, the number of simultaneous requests must be scalable without causing conflict that hinders your ingest. 

For enhanced efficiency, your database should isolate the query processing from the ingest and employ SQL. Even better is a real-time analytics database that can execute rollups, searches, aggregations, joins, and other SQL actions as the data is consumed.

6. Change Data Capture (CDC) Tools

You can continually capture changes made in your operational database (like MongoDB). The problem is that data warehouses are immutable, making it difficult to modify the data and maintain real-time synchronization between the operational database and the data warehouse. This even happens with some of the most well-known cloud data warehouses. To solve this, you can use Equalum. Our solution enables you to continuously access real-time data, track changes, and apply transformations before ETL using built-in CDC capabilities.

High-Speed Data Delivery Becomes a Reality With Equalum

The world revolves around real-time data streaming, which is why reviewing your architecture is more important than ever. Choosing the right components will set your business up for future success by ensuring you can scale up and be flexible as needed. Whether you are planning to migrate to the cloud, harness real-time insights for business KPIs or another use case, data streaming can help you achieve your goals. 

Equalum steps in to support businesses on their cloud migration or adoption journey by enabling continuous access to real-time data using built-in CDC capabilities and streaming ETL. With Equalum’s help, better visibility and fast data delivery can be a reality. Want to know how it works? Book a demo today

Real-time Data Streaming: What is it and How does it work?

Data streaming in real-time has seen an exponential increase, and more than 80% of organizations report that real-time data streams are critical to building responsive business processes and improved experiences for their customers. Data streaming helps companies gain actionable business insights, migrate and sync data to the cloud, run effective online advertising campaigns, and create innovative nextgen applications and services. But in order to act on events and data as soon as they happen, you need a data infrastructure built for real-time streaming.

The need for real-time data

When a business runs in real-time, the need for real-time data becomes increasingly apparent. Use cases we see around security/threat management, customer activity tracking, and real-time financial data are all excellent examples of this.

Health care organizations are increasingly relying on real-time data when making decisions about patient care. IoT sensor analytics, cybersecurity, patient communication, insurance, research, and many other domains are impacted by real-time data. This data needs to be analyzed immediately, and is often transformed before reaching the target stores (i.e., real-time ETL). Real-time data streaming is therefore an integral part of modern data stacks.

Common Streaming ETL Use Cases

360-degree customer view

A common use case for streaming ETL (also called real-time ETL) is achieving a “360-degree customer view,” particularly one that enhances real-time interactions between businesses and customers. An example of this could be when a customer uses the business’ services (such as a cell phone or a streaming video service) and then searches their website for support. This data is sent to the ETL engine in a streaming manner so that it can be processed and transformed into an analyzable format. Raw interaction data alone may not reveal insights about the customer that could be gained from ETL stream processing. For example, the interactions might suggest that the customer is comparison shopping and might be ready to churn. Should the customer call in for help, the call agent has immediate access to up-to-date information on what the customer was trying to do, and the agent can not only provide effective assistance but can also offer additional up-sell/cross-sell products and services that can benefit the customer.

Credit Card Fraud Detection

A credit card fraud detection application is another example of streaming ETL in action. When you swipe your credit card, the transaction data is sent to or extracted by the fraud detection application. The application then joins the transaction data in a transform step with additional data about you and your purchase history. This data is then analyzed by fraud detection algorithms to look for any suspicious activity. Relevant information includes the time of your most recent transaction, whether you’ve recently purchased from this store, and how your purchase compares to your normal spending habits.

Streaming Architecture and key components

Streaming ETL can filter, aggregate, and otherwise transform your data in-flight before it reaches the data warehouse. Numerous data sources are readily available to you, including log files, SQL databases, applications, message queues, CRMs, and more that could provide valuable business and customer insights.

Stream processing engines use in-memory computation to reduce data latency and improve speed and performance. A stream processor can have multiple data pipelines active at a given point, each pipeline comprising multiple transformations. Each transformation leads to another transformation in the chain, with the result of this chain serving as input for the next transformation. There can be a wide variety of data producers, such as Change Data Capture (CDC), a technology that captures changes from data sources in real-time, as well as a wide variety of consumers, such as real-time analytics apps or dashboards.

The goal is to achieve a streaming latency of 1 second or less for over 20,000 data changes per second for each data source.

Data transformation during stream processing

The aim of streaming ETL or stream processing is to provide low-latency access to streams of records and enable complex processing over them, such as aggregation, joining, and modeling.

Data transformation is a key component of ETL. The transformation includes such activities as:

  • Filtering only the data needed from the source
  • Translating codes
  • Calculating new values
  • Splitting fields into multiple fields
  • Joining fields from multiple sources
  • Aggregating data
  • Normalizing data, such as DateTime in 24-hour format

When working with streaming data, it is often necessary to perform real-time data transformation in order to prepare the data for further processing. This can be a challenge due to the high volume and velocity of streaming data. The task can, however, be accomplished through the use of a number of techniques.

Data filtering refers to the act of limiting what data should be forwarded to the next stage of a stream processing pipeline. You may want to filter out sensitive data that should be handled carefully or that has a limited audience. In addition to data quality and schema matching, filtering is commonly used to ensure data quality. Finally, filtering is a special case of routing a raw stream into multiple streams for further analysis.

In some cases, a stream may still need to be restructured using projection or flattening operations after it has been transformed into structured records. These kinds of transformations are most commonly used to transform records from one schema into another.

Conclusion

Streaming ETL has emerged as the most efficient, effective method of real-time data integration when transformations are required, and supports critical business use cases by integrating with business intelligence products, AI, machine learning, and intelligent process automation (IPA) workflows.

Learn more about streaming ETL by downloading our whitepaper here.

Batch ETL vs Streaming ETL

We live in a data-driven world where anything an individual does online turns out to be data. Be it simple click-throughs or complex transactions, the network keeps track of all the actions. This quickly generated data is collected, processed into desired formats, and stored in the target repositories for future reference.

In this article, we’ll explore a few techniques of ETL and have a detailed analysis on Batch ETL vs Streaming ETL.

What is Extract, Transform and Load?

The ETL process has been in existence since 1970. ETL is the acronym of ‘Extract, Transform and Load’ that denotes the three steps of this technique.

  • Extract: The extract function collects data of all types from various origins. The gathered data may be structured or unstructured i.e. database, CSV files, multimedia, datasets or numerical, and many more. This data is imported and then consolidated into a single depot.
  • Transform: Transform operation converts the data collected from numerous sources into a format suitable for further operations. This process is the most important part of ETL as data transformation remarkably boosts data integrity. An example could be the removal of duplicates from data. After transformation, data becomes fully compatible and ready.
  • Load: Load operation stores these converted forms of data into a single database or data warehouse, making it easy to access for upcoming analysis and predictions. There are two fundamental types of approaches for loading data: i.e. Full load and Incremental Load.

In full load, all transformed data is loaded into a destination e.g. database/data warehouse. This option has its limitations for growing data as, with time, datasets would become very difficult to handle.

Incremental load, on the other hand, is more feasible for day-to-day operations even with exponentially growing data. We would only load the changed/affected data into our destination.

Data migration and testing are the primary applications of ETL that migrates data from one server location to another. Some other cases like Data Integration and Data warehousing techniques also employ ETL to create a bridge between applications by incorporating the data. Business intelligence, testing, networks, and a few other domains highly count on ETL.

Batch ETL

Data experts started working with batch ETL techniques in the early 70s. Batch ETL is better suited for organizations with heavy data loads that aren’t relying on accessing data in real-time. The batch ETL gathers data from all the possible sources at regular intervals from applications, websites, or databases.

There can be numerous ways to make data batches e.g. hourly/daily/weekly etc. depending upon the business requirement. Each batch in this process holds a large pile of data. Before loading the data into the target data warehouse, this function will transform the collected data by a method suitable for their business needs.

Batch ETLs are always based upon some time or triggers. For example, we can schedule batch ETLs recursively according to our need using any scheduling tools or it can be triggered based upon some event. Also, there can be a trigger that as soon as new data arrives in the source folder, batch ETL starts.

Applications of Batch ETL

Industries like chemical production, textiles, banks, payroll processes, and hospitals use the batch ETL technique for various use cases, where updating the data in real-time is not necessary. Weekly reports, historical analyses and yearly reviews are examples of data processing that doesn’t rely on real-time data access.

The Batch ETL function will collect and load the data in increments into a data lake or data warehouse. The time duration between batch loads may vary according to the use case requirements, workload, and the tool we opt to use for the procedure.

BENEFITS

Simple to implement:
This system does not need to closely observe the recently generated data, and so the implementation process is simple. In most cases, batch processing is preferred due to its simplicity. Process monitoring also becomes easy.

Cost-efficient:

The expenditure of Batch ETL is reasonable as it employs traditional methods and executes them repeatedly for all the batches.

Compatible with traditional systems:
There are a few organizations that still use legacy systems and software that are not compatible with any advanced technique of ETL. Batch ETL is compatible with such systems.

Large Volumes of Data:
Batch ETL can be a viable option when we are dealing with huge volumes of data that do not need to be delivered in real-time.

Shortcomings

  • As batch ETL works with a huge amount of data, a slight failure in one set of data could possibly destroy the operation of the whole pile as well. Failure in one row among 100 rows will eventually result in the failure of the remaining 99 rows as well.
  • If the system crashes at the eleventh hour of Batch ETL processing the entire data stored for operations would fail.
  • Organizations with monotonous operations will mostly employ Batch ETL. In case any new data type enters, the system will not recognize and cause inaccuracy.

Tools and Frameworks

  • Alteryx, IBM InfoSphere DataStage, Microsoft SSIS, Talend DI and Oracle Data Integrator are the popular tools employed in Batch ETL.
  • Google Big Query, Map Reduce, and Red Shift are some of the frameworks that support the Batch ETL process.

Streaming ETL

The streaming ETL process is highly preferred by industries that produce back-to-back data, similar to a rushing river stream. This is a real-time streaming process as they work on the data of the nearest timestamp. This ETL structure will neither stay idle for a long time nor wait till the data lake is filled. Instead, it starts executing the extract, transform and load operations as soon as the data streams in. The data may differ in size, flowing speed, source, and type. More than 60% of industries currently utilize real-time data processing.

Jobs for streaming data processing run all the time and process the incoming data into the destination directory continuously. As opposed to batch processing, the size of each data chunk is small but the real difference is that it is processed in real-time.

So, we can say that data will always be up-to-date when we use streaming ETL processing. The business end-users will have a clear picture of data demographics at any given moment and any issues that come in the way can be handled and resolved quickly and efficiently.

As the name shows, streaming data comes in the form of streams from the source. We can use tools like Kafka, ActiveMQ, RabbitMQ, etc. for generating these real-time data streams.

Streaming ETL methodology uses stream-based data pipelines to handle the continuous data flow from source to destination. These pipelines pull data from various sources and load the processed data into cloud storage such as Amazon S3. Tools for performing streaming ETL and for converting from batch to streaming applications are available in various markets.

Applications of Streaming ETL

Industries that heavily depend on live data choose Streaming ETL. Applications like weather forecasting, ticket booking, banking fraud and share market, need to update the data streaming into their target systems every second.

What if there is an update delay in a ticket booking system? The system might present that there are still 3000 tickets available, when in truth only 500 remain. The negative domino effects of inaccurate, unreliable and untimely data can range from dissatisfied customers to significant revenue loss. Streaming ETL can enable businesses with trusted data in real-time.

Benefits

  • Rate of speed: The attractive feature of Streaming ETL is speed. Streaming ETL offers continuous throughput, and in some use cases, lower latency than Batch ETL as well.
  • Compatible with new technologies: Latest technologies like Cloud and Business Intelligence use Streaming ETL to extract, transform and load data to target locations.
  • Minimum delay: The data processing starts at the very instance of its arrival. Streaming ETL makes sure the user leverages the data right away.
  • Data-Driven Insights: Streaming ETL helps organizations leverage data coming into their systems in real-time, enabling better tracking of data demographics and patterns allowing for more powerful, data-driven decisions to guide the business.

Shortcomings

  • As this model is working with live data, there will be no time for recovery.
  • Streaming ETL alone cannot read the data in the repairing process. Hence one should look for a tool that also offers a modern, multi-modal Change Data Capture component with high availability and failover protection if systems go down.
  • This advanced operation requires highly capable platforms and hardware so performance, latency and throughput along with ease of use must be evaluated.

Tools and Frameworks

  • Kafka, Apache Spark, Vertica, Apache Flume, and Apache Flink are the tools available in this market.
  • Apache Spark is the best open-source framework used in streaming ETL because this framework can set multiple nodes to handle petabytes of data without any issues.

Batch ETL vs Streaming ETL

Real-time Scenario:


Cause:

Netflix is a streaming company that presents 450 billion events that have 100 million audiences in over 190 countries. In 2017, Arora, a senior data engineer presented a paper on “Migrating the Netflix architecture from Batch processing to real-time streaming as she felt Netflix would function better in streaming architecture to entertain users with live updates.

Migrating from Batch ETL to Streaming ETL:


The input data that are stored in s3 buckets in Batch ETL should be allowed to pass through streaming pipelines to proceed with the simultaneous operations.

The data engineering team of Netflix explored Streaming ETL solutions vs purely batch ETL as that data processing technique could tolerate mass data loads and handle the latency, throughputs, and other metrics required.

The methodology that replaced Batch ETL is “micro batching” which is a sub-model of streaming ETL. As the organizations worked with Batch ETL, it is easier to switch them into micro-batches where the time duration comes down from hours to minutes or even seconds.


Netflix needed to customize real-time events like logs and transactions effectively.

Results:

As streaming ETL doesn’t require as much storage as batch ETL, the cost of storage to use ETL was significantly lower. The application required less turnaround time to cope with the speed of the data generation. The application integrated with real-time systems and promoted real-time auditing. The application became efficient in training with new machine learning algorithms.

Conclusion:

Although some say that Batch ETL is dead, many organizations still leverage Batch processing for specific use cases not dependent on real-time data. Batch is still frequently used for migrating large data sets, particularly in traditional industries where Streaming ETL is simply not feasible.

Streaming ETL does offer real-time data processing of rapidly changing data sets. Add Change Data Capture into the mix, and the power to capture changes to the data as they happen, and only stream those changes versus the entire data set, makes the Streaming ETL approach all the more dynamic and powerful.

The most vital aspect of data processing is flexibility. Different projects come with different requirements, and each solution for processing data must be evaluated based on your use case. Ideally, you can explore a data integration solution that supports all of your core, data use cases to provide flexibility and a future-proof approach to ingestion.

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