Building a data analytics stack for big data

Analyzing data, finding answers, unlocking insights — this all sounds great, but how can your business get there? Everything starts with a data analytics stack: the technologies needed to take your data from its source all the way through analysis. Learn about how your enterprise can implement the processes needed to analyze big data, and how to create an analytics stack that suits your business needs.

What is the data analytics stack?

A stack is a set of component, modular technologies used to build a larger application. A data_ _analytics stack is the set of technologies used to build an analytics system to integrate, aggregate, transform, model, and report data from disparate data sources.

The layers of the data analytics stack depend on one another to create a functioning analytics system. The data analytics layer depends on a data warehouse and sound data modeling. Those, in turn, depend on a robust data pipeline for ingesting data. The data pipeline depends on integrations with data sources.

Data analytics

The data analytics layer of the stack is what end users interact with. It includes visualizations — such as reports and dashboards — and business intelligence (BI) systems. Data scientists and other technical users can build analytical models that allow businesses to not only understand their past operations, but also forecast what will happen and decide on how to change the business going forward.

The BI and data visualization components of the analytics layer make data easy to understand and manipulate. BI software such as Tableau, Looker, and Microsoft Power BI provides visualizations and tools that allow users to make data-driven business decisions.

Data modeling

The data modeling layer structures and organizes data. Data modeling supports data analytics by allowing users to choose and organize data for querying. Data modeling tools include SQL, dbt, and Dataform.

An organization can facilitate analytical modeling by building an analytical base table (ABT), a flat table created by cleaning various data sources and aggregating them. An ABT allows data scientists to work off of clean and consistent data, affording better performance and accuracy.

Data warehouses and data lakes

A data warehouse is a centralized location for holding data from a variety of sources. An enterprise can transform and model this data, then build visualizations with data analytics and business intelligence software.

Historically, these systems were built on complex on-premises hardware. However, enterprises are now moving to cloud data warehouses to take advantage of their scalability and reduced maintenance overhead in comparison to on-premises warehouses. Enterprises can choose from a variety of robust cloud data warehouses, including Amazon Redshift, Google BigQuery, Microsoft Azure Synapse, and Snowflake.

Data lakes, an alternative to the data warehouse, are used to store large amounts of raw data to accommodate a variety of use cases.

Data ingestion

To get data into a data warehouse, it must first be replicated from an external source. A data pipeline ingests information from data sources and replicates it to a destination, such as a data warehouse or data lake. These data sources are the applications, databases, and files that an analytics stack integrates to feed the data pipeline.

ETL and ELT

ETL (extract, transform, load) and ELT (extract, load, transform) are the processes that pull data from its source, optionally transform it as necessary, and store it in the data warehouse. Cloud data warehouses work best with ELT, as, unlike on-premises warehouses, they have the capability to handle data transformation in addition to data analytics. ELT allows users to transform data selectively once it is in a cloud data warehouse, rather than as a step in the data pipeline.

Building your own data pipeline?

There are two ways to build a data pipeline for data ingestion: do-it-yourself (DIY) and using a prebuilt tool.

If your enterprise decides to try the DIY route, data engineers can use programming and scripting languages such as Python, Ruby, Go, Java, or Bash to build custom ETL jobs. Unfortunately, building your own data pipeline adds a huge burden of infrastructure development and maintenance.

Your organization’s data experts can avoid spending their time reinventing the wheel by using third-party solutions such as Stitch and other SaaS data pipeline platforms, which automate away the infrastructure and allow your enterprise to focus on analytics. ETL platform developers also handle changes over time, so these services require less maintenance than a DIY pipeline.

Integrated vs. best-of-breed approaches

An integrated analytics platform incorporates all layers of a data analytics stack, while a best-of-breed approach uses different vendors for each layer of the stack to create a functioning analytics platform.

The decision between integrated and best-of-breed solutions comes down to a variety of factors, including ease of implementation, maintenance issues, and the features your enterprise requires in its stack. A best-of-breed approach offers the ability to control all the aspects of your data analytics stack, but an integrated solution may be easier to implement and maintain.

Connect your data analytics stack with Stitch

You can't build a data analytics stack without a reliable pipeline for ingesting data. Stitch provides a simple data pipeline for replicating data from more than 100 sources to the cloud data warehouse of your choice for analytics. Try Stitch for free.