Staying competitive in business relies on having the latest insights based on the most recent data. But before you can run an effective analytics program, you need to make sure that all the necessary data is in the same place. So, moving your data to the cloud data warehouse of your choice, such as Snowflake, Amazon Redshift, Azure Synapse, Databricks Delta Lake or Google BigQuery, plays an essential role in being able to run analytics.
It is likely that your data currently resides in multiple different siloed systems. That can make migrating the data to a central cloud data warehouse time consuming and cumbersome. This is where a data loader can help you save time and money. With high-speed data loading, you will be able to accelerate the overall analytics process.
What Is a Data Loader?
A data loader facilitates the process of accessing and moving your data from multiple sources into a central location, such as a cloud data warehouse. A data loader supports high-speed, high-volume data loading. It expedites your data processing by helping you upload practically any data, in any format, from any system, at any volume and velocity. It also automatically keeps up with your source data and schema changes to enable real-time insights.
Your data may currently be living in third-party systems or on-premises. Either way, a data loader enables you to move that data to a cloud data warehouse with no install, no set up, no code and no DevOps needed.
What Are the Benefits of a Data Loader?
Using a data loader takes the pressure off your data engineers. It enables them to build and maintain scalable data pipelines at a speed that keeps up with the demand for data insights. This frees up their time and energy to focus on other priorities that drive business value.
And using a data loader removes barriers related to your analytics program. It increases accessibility to other users beyond expert coders. A simple, wizard-driven experience eliminates the learning curve that typically accompanies new technologies. This makes it easier to share vital, up-to-date customer analytics with other departments, such as finance, sales and marketing.
Implementing the use of a data loader also prepares your organization to grow in the future. It serves as an easy steppingstone to move to full-scale data integration when you are ready.
How Does a Data Loader Work?
Let’s take the example of Informatica’s Data Loader. It works in three simple steps:
- Connect your source
- Select your target
- Choose to run now or schedule for later
The tool is easy to learn and use, with a wizard-driven experience. It will enable you to move your data in as little as five minutes. It supports all major cloud data warehouses, including Snowflake, Amazon Redshift, Azure Synapse, Databricks Delta Lake and Google BigQuery. And with out-of-the-box connectivity, Informatica’s Data Loader is ready to connect to most common third-party sources, such as Marketo and Salesforce. The library of connectors is regularly maintained and growing over time.
Best of all, the tool is free for you to use indefinitely. There's no need for budget approval and no risk of unexpected expenses. You can allocate your funds in other ways.
Informatica Data Loader is a free, fast and frictionless way to load data into the cloud data warehouse of your choice. Get started today to future-proof your data program for free.
What Features Should I Expect from a Data Loader?
- Scheduling – A data loader should offer the option to schedule your tasks. Schedule tasks upon creation or to edit existing tasks to run on a schedule for Snowflake, Google BigQuery, Azure Synapse, AWS Redshift and Databricks. Select your time zone and frequency to schedule. You can always adjust it as business needs change.
- Batch – Batch loading your data comes in handy in many cases, such as when you’re undergoing a lift and shift. Informatica’s Data Loader performs parallel processing to complete your batch load from source to target. It also offers over 30 connectors for your data sources and your cloud-based targets, such as Snowflake, Databricks, Google BigQuery, Azure Synapse and Amazon Redshift.
- Automation –Automation is helpful for many aspects of data loading, including sending notifications and metering usage. Automate notifications to multiple emails on the status of task execution; i.e., failure, warning or success. In the tool, automated metering also can help you monitor your usage to understand the number of rows processed during a cycle. This can show the amount of data processed from multiple sources to targets without requiring manual tracking.
- ETL – A data loader should be able to assist with the extract, transform and load (ETL) process. With Informatica’s Data Loader, you can customize and fine-tune your source data using custom properties. You can also exclude selected fields from every object in a single task, define your customized filters and add primary keys and watermark fields to perform the incremental load to your target.
Learn more about data loader and get involved:
- Join the Data Loader community
- Read the Google Blog
- Check out our partner announcements:
- Watch videos to learn more about your use case: