How Kafka Adds Value in Big Data Ecosystems

In the past few years, there’s been increasing growth in the use of big data tools to serve a variety of business issues and solutions. The realm of big data is getting popular by the day, and so is the demand for technologies linked to this massive ecosystem.

Oftentimes, analytics is described as the most complex process linked to big data, as prior to performing data analytics, the data must be integrated as well as made accessible to enterprise people. However, with so many big data concepts/solutions out there, it is still unclear what they exactly are, what the differences are, or which one is the best. This is where Kafka comes into play.

Apache Kafka is a highly scalable publish-subscribe, aka pub-sub system. Here, users are able to publish a big chunk of messages within the system, which are then consumed via a subscription, all in real-time.

In today’s article, we’re going to explain what makes Apache Kafka so popular and how it adds value in big data projects.

Big Data — Explained

The big data ecosystem is a mixture of structured, unstructured, and semistructured data conducted by businesses that are mined for critical information and utilized in predictive modeling, machine-learning projects, and several other modern analytics applications.

Today, systems processing and storing big data are becoming a common element of data administration architectures in companies. Usually, big data is characterized via the 3Vs: the extensive volume of data in different environments, the variety of types of data stored inside big data systems, and lastly, the velocity at which all data are generated, conducted, and processed.

In 2001, Doug Laney, a data analyst at Meta Group Inc, first discovered these characteristics, which were later popularized by Gartner in 2005. Recently, a number of different Vs have been included in various descriptions of big data— these include value, veracity, and variability.

While big data does not equate to specific volumes of data, its deployments often include gigabytes, terabytes or even petabytes, of data obtained over time.

How Does Big Data Work?

Before companies can consider big data for their needs, they should also consider how big data flows amid multiple locations, systems, sources, owners, users, etc. There are 5 key steps when it comes to managing this big “data fabric” comprising of both traditional and structured data, alongside semistructured and unstructured data:

Creating a strategy
At an extensive level, a solid big data strategy can be a meticulously designed plan that will help you identify and enhance the way you collect, store, control, share, and utilize data both in-and-outside your business. A proper big data strategy can set the ground for company success among a large volume of data.

As you’re crafting a strategy, it is vital to consider your current and future marketing and business goals, as well as technology initiatives. This indicates treating big data as any other critical business asset instead of simply a byproduct of various applications.

Identifying big data sources

  • Streaming data stems from the IoT and a variety of connected devices flowing into IT systems, ranging from wearables, medical devices, smart cars, industrial equipment, etc. As the big data arrives, you can choose to study and analyze it, determine which data you should keep or which should be eliminated, and which data needs more analysis, among other things.
  • Social media data comes from regular interactions on Instagram, Facebook, Twitter, YouTube, etc. This involves a large chunk of big data as the image, video, text, voice, and sound form— effective for sales, marketing, and support purposes. The data is also often in semistructured or unstructured form, thus posing a challenge for collection and analysis.
  • Publicly accessible data stems from a large number of open data sources, e.g., the home of the U.S. Government’s open data: data.gov, the EU Open Data Portal, the World Factbook, etc.
  • Other big data comes from cloud data sources, data lakes, suppliers, consumers, and so on.

Accessing, managing and storing the data
Today’s advanced computing systems offer the power, speed, and flexibility required to rapidly access extensive types and volumes of big data. Besides reliable access, businesses also require methods for combining the data, securing data quality, implementing data storage and governance, and getting the data ready for analytics.

While some data are stored on-premises inside a conventional data warehouse, there are plenty of flexible, cost-effective options for keeping and managing big data through data lakes, cloud solutions, Hadoop, etc.

Analyzing big data
Today’s high-end technologies like in-memory analytics or grid computing allow businesses to use some, if not, all of their big data for different types of analysis. Another method is to identify beforehand which data has more relevance prior to analyzing it. Regardless, big data analytics has enabled these companies to gain both value and insight from data. Nowadays, big data feeds modern analytics endeavors like artificial intelligence.

Making data-driven decisions
When the data is managed well and can be trusted, it almost always results in trusted analytics and decisions. In order for businesses to stay on top of industry trends, they must reap all the benefits of big data. Enterprises need to work in a data-driven manner— employing decisions as per the evidence conducted from big data, not gut instinct.

Being data-driven has a ton of advantages. Data-driven companies usually perform better. They are operationally a lot more predictable compared to others and can be as profitable as estimated.

Why Is Big Data So Important?
Businesses employ the big data stored in their systems to enhance operations, offer better services to their clients, create modified marketing campaigns as per specific customer needs, and eventually, boost profitability.

Companies that use big data boast a notable competitive advantage compared to businesses that don’t, as they can make faster, more informed decisions that are based on adequate data analysis.

For instance, big data offers businesses valuable insights regarding their clients, which can be applied to improve marketing tactics and campaigns to boost consumer engagement, and therefore, higher conversion rates.

Also, using big data allows businesses to become growingly customer-centric. They can use both traditional and real-time data to evaluate the evolving customer preferences, thereby allowing businesses to personalize and enhance their marketing tactics and become a lot more responsive toward consumer needs and desires than ever before.

Furthermore, medical researchers use big data to determine the risk factors of a particular disease, while doctors use it as a means to diagnose conditions and illnesses in patients.

Additionally, data obtained from social media, electronic health records (EHRs), and other reliable sources may offer government agencies and healthcare organizations updated information regarding infectious disease outbreaks or threats.

On top of that, big data helps gas and oil companies identify suitable drilling locations, monitor various pipeline operations, and so on. Similarly, utilities use big data to track different electrical grids.

Several financial services corporations use big data for risk control and real-time market data analysis.

Manufacturers and shipping businesses use big data to evaluate their supply chains. It also helps them optimize all delivery routes.

Lastly, big data has a few additional governmental uses, such as crime prevention, emergency response, smart city initiatives, etc.

Kafka — Explained

To obtain real-time analytics, Apache Kafka is mostly employed in stream-based architectures. Because Kafka is a scalable, durable, fast, and a fault-tolerant pub-sub messaging system, it is highly preferred in situations where JMS, AMQP, and RabbitMQ cannot be considered, given the responsiveness and volume.

Kafka boasts higher reliability, throughput, and replication components, making it applicable for stuff like tracing service calls or tracing IoT sensor data in which a conventional MOM may not even be an option.

Kafka works seamlessly with Flume or Flafka, Flink, Storm, Spark, Spark Streaming, and HBase for real-time analytics and processing of streaming data. The data stream is used to feed Hadoop big data lakes. Moreover, Kafka brokers support large message streams for low-latency follow-up analyses in Spark or Hadoop. And Kafka Streams, which is a Kafka subproject, is often used for a vast number of real-time analytics.

What Are the Use Cases of Kafka?

To put it simply, Apache Kafka is widely used for site activity tracking, stream processing, implementing data in Spark and Hadoop, metrics collection/monitoring, real-time analytics, log aggregation, CEP, CQRS, error recovery, replay messages, and microservices.

Who Actually Uses Kafka?

Many large companies that handle large amounts of data make use of Kafka. LinkedIn, the platform where Kafka was originated, employs it for tracking activity data, operational metrics, etc.

Square uses it as a bus that moves each system event to individual data centers (custom events, logs, metrics, etc.) and to perform CEP alerting systems. Twitter uses Kafka as a part of Storm, which is the platform’s stream-processing infrastructure.

Kafka is also utilized by other big-name companies like Uber, Spotify, Tumblr, PayPal, Goldman Sachs, Box, Cisco, Netflix, and Cloudflare.

What Makes Kafka So Increasingly Popular?

Kafka offers operational simplicity. Setting it up is a relatively easy process. Once everything’s set, Kafka is easy to use. Figuring things out shouldn’t take forever, even if you’re a novice user. However, that’s not even the reason behind its massive popularity.

What makes Kafka stand out is its top-tier performance. Kafka is stable and offers reliable durability. It boasts a flexible pub-sub/queue that perfectly scales with N-number of customer groups. Kafka also has powerful replication and offers producers modifiable consistency guarantees and preserved ordering guarantees.

Additionally, Kafka works seamlessly with systems containing data streams for processing and allows them to aggregate, remodel, and store into other repositories. That said, if Kafka wasn’t as fast as it is today, none of these characteristics would matter.

The Role of Kafka in the Big Data Ecosystem

Data Collection

As you’re aware, collecting data is vital for analytics. Systems generating data make the process of collecting data a challenge. Many tools out there allow businesses to make their decisions according to the new data available.

The data growth rate, however, is exponential, considering there are a large number of devices and sensors connecting to the web every day. As this number is increasing on a regular basis, we’ll have to move, catalog, and analyze even larger numbers of data.

Several companies out there are taking initiatives to use the data and provide their customers with a better experience.

Data growth was recently quantified, which gives us predictions, like:

  • In about eight years, the average user will use a connected device around 4,800 times per day.
  • Data should be available as soon as the user requires it, no matter where they are. By the year 2025, more than 25% of data generated in the Global DataSphere are expected to be real-time in nature. Furthermore, IoT real-time data should make up over 95% of this.

Therefore, the significance of collecting data goes beyond business optimization and decision-making. It impacts the journey and goals a company will necessitate.

Limitations

For many years, databases have been the go-to solution where users store and manage the data. To this day, database vendors are adding more new features to enhance the work capabilities inside, e.g., search, streaming, and whatnot.

Over time, this model has lost its efficiency and credibility. For starters, databases are costly storage systems. With the exponential growth of data, collecting data has become a crucial factor.

That said, researchers are expanding their range in collecting data by considering other data sets, e.g., operational metrics, user tracking records, application logs, and so on. These sets of data are beneficial in terms of generating unique insights. Because traditional databases generally depend on high-end storage solutions, storing the data sets becomes a lot more expensive.

Also, since data scientists are diving deeper to obtain more precise insights from analyses, more decision points/ features are accumulated. Hence, the limitations of traditional databases become more apparent as it’s difficult to incorporate the said features while maintaining the old ones.

Emergence

In order to overcome such limitations, people began developing specialized systems that are designed to do one thing—and do it well. Due to their simplicity, it is easier to formulate them as shared systems that operate on hardware like a PC.

These are open-source systems, making them incredibly cost-effective as opposed to the costly traditional databases. Furthermore, these systems are specialized, as mentioned. Therefore, they are continually developed and enhanced as new concepts are implemented daily.

One of the pioneers in this method, Hadoop specializes in processing offline data by implementing a distributed file system (HDFS), as well as a computation engine (MapReduce) to store and process data in large batches.

By utilizing HDFS, businesses can now easily collect additional sets of data that are useful, yet too costly to store inside databases. With the help of MapReduce, people are able to create reports and conduct analytics on the new data sets, all under a cost-efficient manner. Specialized systems like these allow businesses to derive more insights and develop new applications that previously were nothing more than a vision.

Although these systems have transformed the world of Data Analysis, it brings a few challenges.

Keep in mind that there are various data types depending on different use cases and contexts. Sometimes, the same set of data must be fed into several specialized systems for a variety of operations. For example, in some situations, it is vital to identify and analyze trends according to the traditional and real-time data whereas even an insignificant entry often needs to be searched out of a million rows. Application logs can be beneficial for offline log analysis, but it’s equally essential to search separate log entries, making it nearly impossible to create an individual pipeline for collecting data and feed it directly into a suitable specialized system.

Hadoop usually keeps a copy of different data types, yet it’s infeasible to feed the data to other systems fetching from Hadoop. Such a scenario influences the analysis involving the most real-time data that isn’t presented by Hadoop.

This is where we call for Kafka as it offers the following nifty features:

  • Kafka is built as a shared system and is able to store high volumes of data on off-the-shelf hardware
  • Kafka is also designed as an impressive multi-subscription system, meaning the same set of published data can be employed several times
  • Data in Kafka is persisted to disks
  • Kafka is able to deliver messages to batch consumers and real-time simultaneously without performance hindrance
  • Kafka contains built-in redundancy; thus, it can be utilized to accommodate the reliability required for mission-critical data

Turns out, most of the companies we mentioned earlier have constantly adopted a variety of specialized systems. Each uses Kafka as a central platform to consume different types of data in real-time. Often, the same data set is fed into multiple specialized systems.

We allude to this unique architecture as a streaming data platform as shown in the image below. Including new specialized systems in this architecture is convenient as the new system will get its data simply by making an additional subscription to Kafka.

Before 2014, it was all Hadoop. Then came Spark into play. Now, all people are talking about is Hadoop, Spark, and, of course, Kafka— the three equal peers in the data ingestion pipeline of today’s analytical architecture.

In our honest opinion, the biggest advantage of this platform is the fact that we’re able to add additional specialized systems to consume all data published to Kafka. If you made it this far, then you already know how important this is for the advancement prospects of the big data ecosystem.

We’ll likely see more new platforms utilizing a pub-sub system such as Kafka. It is expected to play a critical role as more businesses require high-volume, real-time data processing.

That said, one consequence could be that we might have to rethink the whole data curation process. As of now, a lot of the data curation, e.g., schematizing data and evolving schemas, is put off until after the data is stored into Hadoop. Now, this isn’t very ideal for streaming data platforms, as the same process of data curation would need to be repeated in different specialized systems, too. It is advised to solve data duration problems early on, especially when the data is absorbed into Kafka.

The Kafka Ecosystem

Kafka Ecosystem contains a number of subordinate systems, including Stream Processing, Search and Query, Hadoop integration, AWS integration, Metrics, or Logging.

Stream Processing

  • Samza—this is a YARN-based streaming processing framework
  • Storm—a distributed, fault-tolerant framework designed for real-time computation and processing live data streams
  • Storm Spout—takes information from Kafka and transmits them in the form of Storm tuples
  • SparkStreaming—an extension of the core Spark API, enabling high-throughput and scalable stream processing of live data streams
  • Kafka-Storm—Storm 0.9, Kafka 0.8, Avro integration

Hadoop Integration

  • Flume—boasts Kafka Sink (producer) and Source (consumer)
  • Camus—LinkedIn’s Kafka => HDFS pipeline, used for all LinkedIn data, works seamlessly
  • Kangaroo—a nifty tool that takes data from Kafka using different formats and compression codecs
  • Kafka Hadoop Loader—this is a different interpretation on Hadoop’s loading functionality, not identical to what’s included in the distribution

Search & Query

  • Hive—Hive SerDe lets you query Kafka with the help of Hive SQL.
  • Presto—Presto Kafka lets you query Kafka in SQL with the help of Presto
  • ElasticSearch—this particular project will read messages from Kafka, then process and arrange them in ElasticSearch.

Logging

  • Syslog producer—supports raw data and protobuf with metadata for deep analytics use
  • LogStash Integration

Conclusion

The current trend as we see in the industry demonstrates that multiple specialized systems can and will co-exist in tomorrow’s big data ecosystem. An efficient streaming data platform backed by distributed pub-sub systems such as Kafka will play a significant role in big data’s ecosystem as more and more companies are opting for real-time processing.

That said, every other tool in big data has its fair share of benefits and processing methods. Any real-time solution in big data will be a mishmash of such specialized systems for achieving the required performance.

Most renowned combinations of Kafka would be with Hadoop and Spark. These systems support each other in terms of achieving end-to-end results. While Hadoop meets the requirement for storing data in HDFS and performing analysis, Kafka provides high speeds when it comes to transportation and sharing data to several different locations.

We hope you found this article helpful. If you have any questions or thoughts, feel free to leave us a comment down below.

 

 

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