Big Data development has generated a lot of interest in recent months as businesses have realized they simply cannot compete until they can mine, leverage, and utilize the data they collect and house internally to their advantage.
Despite being more accessible than ever, Big Data application development services are not always understood. In this article, we’ll delve a little deeper into Big Data software development, including how it’s used and how it can benefit businesses like yours.
What is Big Data development?
In order to understand Big Data development services, we have to understand exactly what Big Data is. Big Data is a combination of structured or unstructured data that has been collected by organizations and mined for information and used in machine learning projects or predictive modeling.
Some people prefer to characterize Big Data according to the so-called three V’s: volume, variety, and velocity.
Volume refers to the large volume of data that organizations store in different environments; variety refers to the many different types of data stored, and velocity refers to the speed at which data is generated, collected, and processed. (Some academics have expanded the list of V’s to include veracity, value, and variability).
It’s hard to say exactly how much data equates to Big Data, but it often involves terabytes, petabytes, or even exabytes of information. (Again, some professionals argue that datasets have to reach a size of at least five exabytes for them to be considered “big”).
Big Data application development companies or engineers create systems that can process and store big data or tools that support the use of Big Data analytics.
Why are Big Data engineering services important?
Companies use Big Data to improve operations and customer service, create personalized marketing campaigns, and take other actions that increase revenue and profits. Big Data can provide a significant competitive advantage by providing valuable insights that can be used to fine-tune strategies and increase conversion rates.
With the right analytical tools in place, both historical and real-time data can be analyzed to assess the evolving preferences of customers or clients, which in turn makes business more responsive.
Of course, Big Data isn’t just used by commercial enterprises in the business world. Medical researchers have turned to Big Data to identify patterns in patient information that can help medical professionals spot early warning signs of serious diseases and improve their ability to diagnose illnesses. Data from electronic health records and social media was even used to alert community hospitals and medical centers about recent outbreaks of monkeypox and COVID-19.
In the energy industry, Big Data helps oil and gas companies identify drilling locations and pipeline operations. Financial firms use big data applications for risk management. Manufacturers use Big Data to optimize their supply chains and production, while governments use data for crime prevention and smart city initiatives.
Which data is used?
Data can come from anywhere. Transaction processing systems, customer databases, documents, emails, clickstream logs, medical records, mobile apps, and even social media networks. Machine-generated data such as network and server log files and sensor data from the Internet of Things devices are also used.
Big Data environments can incorporate information from internal systems or external systems, like financial markets, traffic conditions, weather, scientific research, and more. Thanks to computer vision AI, images, audio, and video files can be processed too.
To make sense of Big Data analytics applications, the data analysts and developers must have a detailed understanding of what data is available and what they are looking for. This enables data preparation, including profiling, cleansing, validation, and transformation.
Once data is gathered and prepared, data scientists can run the tools that have been designed for the process against it, including machine learning and deep learning tools, predictive modeling, streaming analytics, and text mining.
Developers may build tools for analytics, including:
- Comparative analytical tools that analyze customer behaviour metrics and customer engagement;
- Social media listening tools that analyze what is being said about a business or product;
- Marketing analytics that provides insight into marketing campaigns and their effectiveness;
- Sentiment analysis reveals how customers truly feel about a brand or company.
Big Data Technology
An open-source distributed processing framework known as Hadoop has been at the center of big data architectures for years, but the development of Spark and MapReduce (Amazon EMR) has pushed it to the side. Today, there is a lively ecosystem of Big Data technologies that can be used for different applications or deployed together to achieve the best possible result.
Big Data developers will also use storage repositories like HDFS, Amazon Simple Storage Service (S3), Google Cloud Storage, and Azure Blob Storage in addition to cluster management frameworks (Kubernetes, Mesos, YARN), and stream processing engines like Flink, Hudi, Kafka and more.
They also need intimate knowledge of NoSQL databases like Cassandra and Couchbase, data lake and data warehouse platforms like Amazon Redshift, Google BigQuery, and Snowflake, and SQL query engines like Drill, Hive, Impala, Presto, and Trino.
Challenges of Big Data Development
The biggest challenge when it comes to Big Data is always designing a Big Data architecture (and processing capacity limitations). Big Data systems must be tailored to the organization’s unique needs in order to be effective. This requires close cooperation between data management and developer teams to create a customized set of technologies and tools. IT managers also have a part to play in ensuring that cloud usage costs do not get out of hand. Migrating on-premises data sets and processing workloads can also become extremely complicated.
At the end of the day, businesses not only need technical expertise that can create big data systems that really work but that provide data that is accessible to data scientists and analysts.
Conclusion
The business value of Big Data initiatives depends on the workers tasked with managing and analyzing it. As it’s often said, Big Data is for machines; small data is for people. Big Data development is the key to breaking data down so that it can be used effectively in practice.