Considering that more people are becoming tech-savvy, enterprises need to focus on data and derive actionable insights as quickly as possible. This is possible only if stakeholders can:
- Cut the noise and distraction of maintaining hardware and software installation.
- Resolve configuration issues and tune the engine as needed.
- Maintain high availability of resources.
- Maintain security.
To do that, it is vital to understand a typical data journey. From end to end, data acquisition and use involve four main stages that can relate to data storage and data processing.
- Data collection is a stage where enterprises collect data from various sources. This data can be structured data, semi-structured data, or unstructured data.
- Data cleansing is a stage where the collected data is cleansed by removing garbage data, unwanted characters, spaces etc.
- Data transformation is a stage where we add more meaning to our data by performing joins, aggregations, converting semi-structured data into structured data etc.
- Data Insights can be extracted once the data is collected, cleansed, and transformed.
Because there is a need to provision, separate storage, compute and analyze data, stakeholders should be able to scale and add to their operations. Different workloads have different purposes and thereby lead to diverse requirements of resources. As such, it is cost-efficient to acquire resources as you need and release them once the job is done.
Snowflake can help you to solve these problems. This cloud-based SaaS platform enables enterprises to reap benefits such as agility, scalability, elasticity, and end-to-end security.
Snowflake segregates storage and processing. This dramatically relieves data scientist teams who run concurrent workloads on different virtual warehouses because they do not have to compete for resources on a single multi-core machine. Ultimately this directly results in getting quicker insights into data.
Spectra is a comprehensive DataOps (data ingestion, transformation, and preparation) platform to build and manage complex, varied data pipelines using a low-code user interface with domain-specific features to deliver data solutions at speed and scale.
Snowflake and Spectra: Illuminate your data processing
Spectra’s low-code interface and Snowflake’s data dynamism offer stakeholders the liberty to build their data pipelines. The intuitive user interface’s drag and drop functionality makes data processing as simple as 1, 2, 3.
Spectra converts data pipelines into Snowflake’s readable code and executes the four data-processing steps for you. It also abstracts the technicalities needed to write and manage stored procedures or SQL into the Snowflake client. Spectra provides an easy way to maintain your pipelines and seamlessly take them to production after validating them in DevOps and QA environments.
An apt example to showcase the power of Spectra is the latest development of Snowpark in Snowflake. Snowflake launched with support to stored procedures for executing jobs, so Spectra used to convert the pipelines into stored procedures before executing jobs into Snowflake. However, over the period, Snowflake released an optimized approach to managing jobs in the form of Snowpark. So, if a customer is on Spectra, they need not worry about these changes. Spectra can do this seamlessly without bothering them with any changes to the pipeline.