Metadata-driven ingestion engine using Spark is a new approach to the old problem of ingesting data in Hadoop.
It has been shown that an ingestion engine can be one of the most time-consuming processes in any large-scale analytics project because it requires so much preparation and integration.
When planning your next project, there are many considerations in terms of how to best approach it.
Using an ingestion engine utilizing Spark can provide you countless advantages.
Metadata-driven ingestion engine is an approach that uses a very different way to ingest data.
It skips the often time-consuming loading and integration processes by using metadata with the files being ingested into Hadoop.
It then performs transformations on this metadata before storing it in HDFS, essentially leaving out all other processes involved compared to traditional approaches of ingesting data from source systems such as relational databases or flat files directly into Hadoop.
Instead of taking raw input records, these engines take information about;
By taking the time to set up a metadata-driven ingestion engine, companies can ensure that they can take full advantage of all available data sources and tools to gain valuable insight into business operations.
The metadata-driven approach has several advantages over traditional methods:
Meta data-driven ingestion engine can be used with any Hadoop processing framework, including MapReduce, Spark, etc.
This is why it makes sense to implement this approach using Apache Spark, which provides higher performance than most other tools in its class.
Apache Spark is an open-source cluster computing framework.
It has become popular in recent years because of its performance which can be up to 100 times faster than Hadoop MapReduce when processing large datasets.
Apache Spark also provides several other advantages over traditional data analysis tools, including the ability to process streaming, interactive, and graph-based data.
Spark is fast because it does not rely on a disk for storing intermediate data, instead leveraging RAM.
It also uses in-memory operations to process datasets whose size doesn't fit into the memory of a single machine.
To reduce the need for moving large amounts of data in and out of disks when operating with big datasets, Spark relies on advanced query optimization techniques.
The application development process for ingesting large volumes of information from various source systems is often very complex due to the need for custom integration with each particular system or format being used and an overall lack of standardization.
The metadata-driven approach has been shown to overcome these problems and improve the overall efficiency of ingesting data from all sources into Hadoop.
Spark is a general-purpose cluster computing framework that has been designed with interactive and iterative applications in mind.
It provides support for streaming data and the ability to process large volumes of structured data efficiently.
Spark's ability to work with several data formats, including JSON and Parquet, makes it possible for users to implement a metadata-driven ingestion engine using their preferred format without the need for expensive transformation at ingestion time.
Spark can be used for both batch and interactive, streaming, or graph-based processing of datasets which makes it a good fit for the ingestion engine.
Spark also provides an effective solution when working with unstructured data sources such as CSV's or social media feeds using the new Structured Streaming API.
Another benefit is its ability to scale across multiple servers due to its in-memory storage capabilities, making it easy to distribute workloads over larger clusters without the need for expensive network transfers between nodes.
This scalability allows us to increase the overall speed of data ingestions and build a more robust solution by distributing the load on multiple nodes.
You should consider implementing a metadata-driven, Spark-based ingestion engine for your organization which will help optimize both productivity and performance while reducing costs associated with custom integrations for each source system.
You must select a practical solution, so your company's business goals are met or exceeded through increased agility and cost savings.
Meta Data Driven Ingestion Engines works best with metadata that is both standardized and well documented.
However, it can be customized to fit specific needs as well.
The point is that the same flexibility that leads many people to choose Hadoop over other technologies because they do not have a pre-defined schema will hold.
If you need help with incorporating metadata-driven ingestion using Spark into your organization, feel free to contact us for more information.