Accelerate data transformation with CloudMile and Fivetran

Data analysts are spending an increasing amount of time collecting and cleaning information from multiple data sources, cutting the time available for real business problem-solving. In fact, 90% of analysts report that several data sources are unreliable, and 68% say they lack time to implement profit-driving ideas. Collecting data from essential operational systems and combining it in a modern data warehouse is the key to gaining actionable insights. Still, as the number of data sources and the volume of data increases, it is becoming essential for businesses to cut the time spent on collecting and cleaning data so that analysts can refocus on their most valuable skills: business insights and problem-solving. 

ETL is a Never-Ending Engineering Cycle

The process of collecting data from a source, transforming it into the correct form, then loading it into the data repository is known as the Extract-Transform-Load (ETL) cycle. Most companies fail at ETL process and struggle turning data into useful insights. That’s because managing multiple data pipelines requires constant mapping of data schemas to load in and to handle new data requests which leaves little time for actual analysis. Therefore, companies are looking for data solutions that help to improve this process. According to the table below, investment dollars are growing in each data category, with ELT and Orchestration accountable for more than 12%. 

Automating data pipelines to the cloud with Fivetran

Analysts need to make the most of their expertise and free up time and resources to gain valuable insights into their data, and to do this, the modern data stack should consist of four parts: 

1. Data source: According to Dimensional Research, over half of companies use 11 or more data sources. Sources can include databases like MySQL, MongoDB, PostgreSQL and web applications such as Salesforce, Google Ads , Facebook Ads and MailChimp. 
2. Data integration: A SaaS Data Integration like Fivetran takes care of the multiple steps in the ELT and automated data integration. This tool empowers the organization to optimize their data strategy to bring in all relevant objects quickly and easily.
3. Cloud data warehouse: A cloud data warehouse like Google BigQuery scales to handle differing workloads and frees the development team from having to maintain the underlying infrastructure. The separation of compute and bandwidth comes into play here, allowing the companies to minimize overall costs.
4. SaaS BI and analytics: A BI platform such as Looker allows enterprises to provide all their teams with a common definition of their metrics to enable the most accurate analysis possible across the entire business.

Why Fivetran?

Fivetran is a SaaS data integration service for companies to extract, transform and load data from different sources into data warehouses. What makes Fivetran different is how it handles both data and schema updates. Automatic data updates ensure a done-for-you service to keep data up-to-date by inserting new data, updating existing data, and soft-deleting data when detecting deletes in the source. The soft deletion is a particularly important feature because instead of permanently deleting your data, it maintains records of your data, protecting your data from mishaps at the data source.

Streamline The ETL Cycle with CloudMile and Fivetran

CloudMile is the first exclusive Google Managed Service Partner in Taiwan, with over 120 certified cloud engineers and support in Singapore, Malaysia, Hong Kong, Taiwan, Philippines, and Indonesia. CloudMile Data Lab Solution opens up the possibility of integration with various technologies through the partnership with Fivetran. Once data lands in a Data warehouse, it brings additional value to businesses including business intelligence, AI/ML prediction and even Data and API monetisation. 

CloudMile recently hosted a webinar with Fivetran to discuss how Fivetran works with Google BigQuery and BI and analytics tools like Looker to reduce the complexity of data aggregation and help data analysts to garner better insights from their data. See the full webinar here: LINK