It’s widely understood that competitive businesses need to be data-driven. Teams across the organization need access to data quickly and with ease.
In order to service this need, it’s crucial to have an efficient flow of data from one location to another. With an ever-increasing volume of data and data sources, the systems that transfer data - data pipelines - are under pressure.
As a result, we’ve seen some key shifts in data pipeline configurations. Modern data pipelines need to be agile, scalable and easily managed.
In this post, we’ll outline what has changed in the data landscape in recent years and how modern data pipelines are adapting. (You can skip ahead for components of modern data pipelines).
Simply put, a data pipeline is a system that transfers data from disparate sources to a destination where it can be used for analysis.
Your business likely has various sources of data related to how people engage with your brand. This might include web sessions in Google Analytics, purchases in Salesforce, customer service interactions in Zendesk, and other data collected in a database powered by MongoDB or PostgreSQL.
In order for analysts and data scientists to gain insights from this data, it needs to be aggregated from multiple sources into one place (typically a data warehouse). Data pipelines are the systems that move data from sources to a centralized destination.
Businesses of all sizes have embraced SaaS as a way to stay nimble. A recent SaaS trends report found that companies spent 50% more on SaaS products in 2020 compared to 2018.
With every new SaaS product adopted by an organization comes a new data source. Most SaaS products boast the ability to collect a lot of data meaning businesses now have access to huge volumes of data from disparate sources.
Wrangling data from an increasing number of sources has its challenges. Data collected from disparate sources will likely be in different formats, including unstructured data. Multiple data sources can also yield conflicting or duplicate data.
Data pipelines need to be more robust to handle these challenges.
As the amount of data collected by organizations skyrockets, data storage costs become a serious concern. In a traditional infrastructure, more data means more hardware, server rooms and staff.
The advent of cloud data warehouses like Amazon Redshift, Snowflake and Google BigQuery dramatically decreased storage costs. Organizations are able to store more data than before without needing to commit to rigid and expensive hardware investments.
In today’s environment, flexibility is growth. Organizations that can make quick, data-informed decisions have a huge competitive advantage over more rigid counterparts.
Traditional infrastructure doesn’t always offer the agility needed. Batch ETL processes, expensive hardware investments and engineering-heavy pipelines all make it difficult to grow and manage your data over time.
Businesses are adapting their data pipelines to meet the need for agile decision making.
Today’s business intelligence teams require near-real-time data. Traditionally, extracting, transforming and loading (ETL) data is done in scheduled hourly or daily batches. This results in stale data and missed opportunities.
Modern data pipelines utilize automation to continuously load and transform data in a data warehouse. This continuous processing enables analysts from across the organization to use data from minutes ago (instead of hours or days) and identify trends in near real-time.
The rise of cloud data warehousing has allowed organizations to become more flexible than ever before.
With traditional on-premises data warehouses, scaling up data sets and workloads means planning well in advance for significant hardware and database management investments. Cloud data warehouses are elastic meaning they allow you to add or remove computing resources immediately, depending on your needs.
Modern data pipelines leverage the elasticity of the cloud, allowing businesses to better handle usage spikes and growth in storage needs. They don’t risk delayed or abandoned analytics projects due to insufficient data warehousing resources.
Teams across the organization need access to centralized data for informed decision making.
Typically, complicated ETL processes have been a bottleneck for teams needing access to new data sets. Analysts have had to rely on in-house engineers to create data pipelines and transform data into the proper format for querying.
In order to truly democratize data, data pipelines need to be simple enough for analysts to manage autonomously.
Modern data pipelines utilize code-free solutions to allow analysts to add sources, manage the data warehouse and customize transformations. They also provide the ability to manage all types of data, including unstructured data.
Data-driven organizations are shifting towards real-time data exploration using modern data pipelines. By leveraging the cloud and automated ETL processes, the most up-to-date data becomes easily accessible across the organization.
Building modern data pipelines doesn’t need to be complicated. At Etleap we created a simple cloud-based ETL solution that can build robust data pipelines in hours rather than months. Data pipelines can be managed with a code-free interface, freeing up engineering time and allowing analysts to work quickly and independently.
See why modern data teams choose Etleap: Request a demo.
