One of the primary operations in memory programming in Apache Spark is shuffling. The process is responsible for managing data […]
Remember the days when all the data which companies analyze used to come from a single relational database. Today, companies are storing data in different souresa as well as a variety of different types of sources like relational databases, NoSQL, Hadoop repositories, etc.
As data sources are evolving, reconciling data across multiple resources has become a challenge. Lets understand the analytics across various sources.
What’s the need for multiple data sources?
We know that different storage systems have different capabilities and thus it becomes crucial for an organization to choose the most appropriate tool for a specific application.
Lets understand this through an e-commerce application-
- Relational databases are used to hold product details and transactions
- Big data warehousing tools like Hadoop and others are used to store historical transactions and ratings for analytics
- Google analytics to analyze the behaviour of customers on the website
- Log data on S3 or Azure blob
The need for multiple-source data analysis
In order to derive the maximum value from any data, organizations generally want to complete a view that connects different sources of data. In e-commerce, it will be great to combine Google analytics with transactional data to understand the pattern.
This multi-source data analysis drives greater value by offering a more complete view as compared to single-source analysis that generally tells one perspective.
Challenges with Traditional multi-source data analysis
Traditional multi-source data analysis requires all the data to be moved to a single data warehouse. This data warehouse could either be a relational database or a big data store. But, the drawbacks is that these types of systems require expensive ETL operations to move the data.
The main reason for ETL operations is to normalize the data from different sources and to have common storage for analytics.
This data movement results in slow analysis and the ETL process creates latency right from when data is updated until new data makes its way, so the data which is latest is not always available. Many organizations suffer as they end up performing incomplete data analysis from less sources or delayed data.
Structured data analysis for big data
When Apache Spark wasn’t a thing, it was believed that big data is only meant for unstructured data. But this is just not true. From what we have experienced at Ksolves, the data of many of the customers who are big data is unstructured or semi-structured. So whatever platform we choose, it has to support structured data processing.
Apache Spark is the prominent big data processing platform which provides structured data analysis as native abstraction. This abstraction supports structured, unstructured, and semi-structured data. As Spark by nature is a structured-first approach, it can combine data from sources like relational databases, Google database and MongoDB. Spark also has the ability to turn unstructured data into structured data.
Multi-source data analysis in Apache Spark
In Spark 2.0, Spark has chosen Datasets/DataFrame which is known for structured data. This allows data read from various sources to single DataFrame. This abstraction combined with Spark SQL gives the ability to unify the data across various sources and allows search-driven query and analytics.
The biggest capability of Spark is that it loads only the data which is needed on-demand from the sources. This helps analysis in an ad hoc manner rather than waiting for complete movement of data in a traditional way.
Natural language query on Apache Spark
All the datasets which are loaded are represented as Dataframes on Spark. This allows us to offer an amazing and intuitive search experience to the user where they can ask queries across multiple sources with a single question. These queries initiated by the user are represented as SparkSQL queries. This analysis can use the data without actually making a copy of the same in a warehouse system.
The futuristic Spark engine allows customers to ask questions of their data without bothering about the origin of the data. This offers customers a clear advantage over the traditional systems and they can make better, more informed decisions.
Ksolves Apache Spark services are a go to solution for all your queries. Our unmatched experienced and certified developers on Apache Spark are talented as well as well-equipped with latest technology to provide you a high-end result. Our customized Apache Spark development and consulting services stand out in the crowd.
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