Reimagine a Simpler ETL Data Pipeline
What is an ETL pipeline?
Extract, Transform, and Load (ETL) pipelines are critical to every enterprise to enable them to extract data from various sources, transform them, consolidate them, and prepare them to be used by downstream applications and services. This consolidated data can help drive improved customer experiences and decision-making within enterprises.
What are the challenges of building an ETL pipeline?
ETL pipelines can be a powerful tool for integrating and processing data, but they also present several challenges that must be addressed to ensure their effectiveness and efficiency.
The challenges vary based on how the data pipelines are built and managed
Self-managed ETL data pipeline:
- Enterprises have to stitch together multiple components such as Kafka, Spark/Flink, connectors, databases, and monitoring tools.
- A multi-vendor approach to building ETL data pipelines can lead to platform inconsistencies that must be compensated at an application level increasing complexity
- Source-driven extractions rely on Webhooks which often fail, and cannot be relied on for mission-critical data collection.
- As elastic scalability can be a challenge, ETL pipelines are designed for peak volumes of traffic resulting in resource wastage and higher operational costs.
- Managing distributed systems concerns during node, zone, or region failures can lead to failed or incomplete processing of ingested data leading to inconsistencies in results.
ETL data pipeline as a SaaS service:
- Processing large volumes of data can lead to higher operational costs as data moves from one cloud to another.
- Enterprises must rely on vendors for data security, privacy, and compliance.
- Mostly focused on analytics workloads and lacks maturity in other areas.
- Do not support a strongly consistent view of data across zones and regions
- Integration with systems and tools, and ensuring that these integrations work correctly can be a significant challenge.
How does Grainite simplify building an ETL pipeline?
Grainite is an integrated event processing platform that provides all the necessary capabilities to build an ETL data pipeline. It provides the required capability to extract, transform and load data from various sources within a single component that can then be deployed and scaled using Kubernetes in any public or private cloud environment.
ETL vs ELT? Which is better?
When designing data pipelines, architects generally have to consider the tradeoff between ETL and ELT processes. For ETL, the process of data ingestion is made slower by transforming data on a separate server before the loading process. ELT, in contrast, delivers faster data ingestion, because data is not sent to a secondary server for restructuring. In fact, with ELT, data can be loaded and transformed simultaneously.
Grainite provides a single component to perform end-to-end ELT or ETL data pipelines
What are the benefits of using Grainite?
With Grainite, architects need not worry about tradeoffs between ETL & ELT processes. Grainite provides a single component to perform end-to-end ELT or ETL data pipelines. It allows you to ingest a very high volume of data and perform the necessary transformations at extremely low latency and then store the materialized views within Grainite data store. Grainite also loads the change data capture (CDC) to a downstream data warehouse completely eliminating the high compute cost for performing ELT transformations within the data warehouse.
Features of Grainite that enable ETL/ELT data pipelines are:
- Integrates connectors, stream ingestion, stream transformation, and storage into one component which can be deployed in any cloud using Kubernetes.
- Grainite can be deployed close to the data avoiding expensive migration costs.
- Delivers extremely low latency ETL/ELT data pipeline to enable ingesting very high volumes of data from both stream and batch sources of data.
- Grainite Tasks are smart connectors that continuously ingest data from external sources. Grainite manages retries, backoffs, and cursor management when ingesting data from various sources. Grainite supports many out-of-box smart connectors to extract data from popular sources.
- Provides strongly consistent state storage that enables other microservice applications to directly integrate with Grainite.
- Application and system-level monitoring are built into Grainite. Dashboards and reports can be directly generated from Grainite without the need for 3rd party monitoring tools.
- Grainite delivers high platform resiliency and scalability; developers do not need to program for this.
- Enterprises are in full control of data privacy, compliance, and security.
To summarize, Grainite provides a converged platform to not only makes it simple to perform ETL or ELT functions, but also offers additional capabilities to store and serve that data directly to downstream applications. All of this can be done in a single platform - accelerating development velocity and drastically reducing costs.
Founded in 2019 by leaders from Google and Citrix, Grainite is backed by top-tier VCs. The team has over 200+ years of collective experience in building large-scale enterprise applications at leading data and storage companies. Reach us at email@example.com to learn more and to schedule a demo.