How to take the pain out of batch data loading

Data architects create the strategy and infrastructure design for the enterprise data environment. Data engineers help implement this strategy. They design, build, and manage data pipelines to provide business-ready data to analysts, data scientists, and decision makers across the organization. These are highly technical roles that require knowledge of architectures, source databases and applications, cloud data lakes and data warehouses, data models and schemas, and programming languages like SQL, Python, and others.  

Without a well thought-out data strategy and efficient data pipelines to move, transform, and prepare data, business is impacted. Connecting to new data sources can take days, weeks, or even months to set up and get working. Fragile data pipelines can break, forcing data engineers to drop everything to get them working again. This creates delays in getting data to those who need it, when they need it. 

The pain is felt not only by data teams, but also by everyone across an enterprise who uses data to run the business. Finance managers need accurate revenue and cost data. Sales leaders need to know where prospects are in the sales process and how the reps are engaging with them. Marketing teams need to know how each marketing channel is performing to bring in new leads. Supply chain managers need timely data on inventory levels and forecasts. Data pipelines collect all of this data into centralized data stores so that everyone can use the data. 

The key ingredient in all of these use cases, historically, is batch data loading—extracting data from data sources and loading it into data platforms more suitable for analytics.  

What are batch data pipelines

Batch data pipelines extract data from a data source and load it into a destination on a set schedule, like once per day. Batch jobs put an additional workload on the source data system, so they often run at night when the user load on the source system is at its lowest. In many large enterprises, batch jobs can take hours to complete.  

For many analytics and reporting projects, this is entirely okay. For example, monthly, weekly, or daily reports on sales revenue, marketing results, store operations, inventory levels, etc. But creating and managing data pipelines across a large and diverse data environment is not easy.  

The pain of loading data

Data architects and data engineers have been critically important over the years, and one could argue that they are even more important today because so much of business success relies on data. They have to deal with a plethora of both technical and business issues across the enterprise. 

Data projects simply take too long and cost way too much. Gartner estimated that data teams report wasting over 50% of their time on data migration and maintenance tasks, instead of delivering on more strategic and advanced analytics projects. Let’s think about that—more than half of the data team’s time is wasted. That is painful. 

Yet, data and data sources continue to grow at exponential rates. IDG and Matillion surveyed enterprises and found that the average large organization taps into about 400 different data sources. And the top 20% of these enterprises use more than 1,000 sources for their BI and analytics programs. 

In addition, SaaS applications are being adopted in record numbers. Enterprises want to connect to and ingest data from the SaaS apps, integrate the data with all of their other information, and run analytics to discover insights that will help them increase revenue, decrease costs, and operate more effectively. 

Writing code to connect to the growing number of data sources is simply unworkable. It takes 4-6 weeks to code up a new connector and a week per quarter to maintain it. Multiply that by a thousand—it’s no wonder that data teams are stretched beyond capacity. Legacy data tools offer little help, as many are not built for the cloud and the modern data stack. 

Also, data loading is often reserved for the IT department. Only IT data engineers can connect to new sources and load data into their cloud data platform. With constant and urgent requests coming their way, the stress on data teams can be overwhelming. You can learn more by reading the ebook The Top 5 Misconceptions About Data Loading.  

How to make data teams, and data, more productive

Most data loading tools today provide pre-built connectors to popular data sources. Vendors typically have between 50 and 150 pre-built connectors. This is great—if you use these data sources. But there are more than 250 CRM applications on the market and over 8,000 Marcom applications. What do you do when you want to deploy a CRM or Marcom application without a pre-built connector? To address this, some vendors help you build a custom connector to any source with a RESTful API (an interface to securely exchange information between systems). This helps a lot! 

Even with pre-built and custom RESTful API connectors, data teams are overwhelmed and overworked—it’s challenging for them to address every incoming request and complete the work in a timely manner. Modern cloud-based data tools help here, too. With minimal configuration and set up, they can generate the data pipeline code needed to extract and load data. This helps data teams become more productive so they can focus on higher level data initiatives.

Consider an enterprise using Salesforce to manage CRM. A sales manager accesses Salesforce constantly throughout the day to monitor sales status, rep engagement, and what’s needed to close more sales. At the same time, the sales manager needs to know what marketing programs are being executed and planned. Integrating email data from tools like Marketo or Outreach into Salesforce helps to align marketing and sales operations and give the sales manager more insights. This requires a data pipeline to automatically extract data from Marketo and load it into Salesforce. 

At the same time, a marketing manager needs to know what marketing content is most useful at different stages of the sales process. They need to combine data from, likely, several different marketing tools as well as Marketo and Salesforce. This can easily be accomplished by creating batch data pipelines that extract data from the various sources and load all of the data into a centralized data warehouse where any of a number of analytics and business intelligence solutions can be used. When a new marketing tool is adopted, the marketing operations team can create a new pipeline to load its data into the data warehouse. 

Empowering more individuals across the enterprise to self-serve data loading alleviates the burden on data teams to get involved with every new data request. This also makes data analysts and line of business managers more productive because they can load and start analyzing data much more rapidly. And, it makes data across the enterprise more productive faster.  

Batch loading made easy

Matillion Data Loader makes batch loading easy. It removes all the barriers to extract source system data and ingest it into your cloud data platform. It’s perfect for organizations that have data coming in from an ever increasing number of sources and need faster, simpler access to data, insights, and analytics. 

“Matillion Data Loader has a simple user interface making data loading easy for analysts and engineers alike.” Patrick Hildreth, Senior Data Warehouse Lead, Cimpress

Matillion Data Loader is “everyone ready.” It empowers data engineers, architects, and even data analysts, AI/ML developers, and data scientists to build robust data pipelines in minutes, without writing code. Pre-built connectors to popular data sources and the ability to create a custom connector to any RESTful API help users build new pipelines to any data source. With Matillion Data Loader, pipelines get created faster and data becomes productive in a timely manner.   

See it in action

To experience the ease-of-use and power of Matillion Data Loader, take a product tour in our interactive demo. This demo lets you be the driver—it guides you through extracting data from Salesforce (while de-selecting Personal Identifiable Information, PII, data), and loading the data into a Snowflake destination. If you would like to see a more detailed demo, you can join our weekly demo or request a demo designed specifically for you and your team.  

If you are ready to unlock more data from multiple sources at speed and scale, try Matillion Data Loader today for free. Once you register, you can load up to a million rows of batch data for free every month, or Start Enterprise trial to test out change data capture in your enterprise.

Andreu Pintado
Andreu Pintado