Your Guide to Creating a Warehouse-First Data Analytics Stack

1

Segment

This approach also helps when a team wants to switch tools and add a new tool to the stack. You won't need to touch the codebase to delete or add any new SDKs. Instead, you can simply remove the tool or integrate the new tool via the event streaming tool's dashboard. Some companies build in-house solutions with tools like Apache Kafka or Amazon Kinesis, but most companies opt for systems that are easy to implement and manage, like Segment and RudderStack.

#Web Service Automation #Data Integration #API Tools 40 social mentions

2

RudderStack

RudderStack can be thought of as an open-source combination of Segment + Fivetran + Hightouch. It's an all-in-one customer data pipeline. It can capture event data from your digital products and send it to your data warehouse and other tools with its Event Stream feature. It uses the Cloud Extract feature to aggregate and correlate non-event data with event data in the data warehouse. And finally, to get data out of the data warehouse, it has a reverse ELT feature known as Warehouse Actions. Also, its source code is publicly available on GitHub, so you can choose to self-host or use RudderStack Cloud for a fee (but you can get started for free).

#Data Integration #Web Analytics #Business Intelligence 22 social mentions

3

Looker

Another tool in the data modeling category is LookML. LookML is embedded as a modeling layer/component into the Looker business intelligence/visualization tool by Google Cloud. LookML bakes in some neat data types that aren't necessarily baked into SQL or your database. However, to use LookML, you need to move data from your warehouse into Looker. This means that the data models you create will be locked in Looker, and you can't send it out to other tools.

#Data Dashboard #Business Intelligence #Data Visualization 14 social mentions

4

Fivetran

The tools in this category are called ELT tools. ELT stands for "extract, load, transform," which are the steps required to take your data from a system like Salesforce or QuickBooks and insert it into your data warehouse. Tools in this category are Fivetran and Stitch.

#Data Integration #ETL #Data Replication 12 social mentions

5

Google BigQuery

BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse built by Google Cloud. It is based on Google's internal column-based data processing technology, "Dremel", and therefore is able to handle both structured and semi-structured data. It is also a fully managed data warehouse and can automatically allocate computing and storage resources as you need them.

#Data Management #Data Warehousing #Data Dashboard 35 social mentions

6

Amazon Redshift

Redshift is a data warehouse owned by Amazon Web Services (AWS). Redshift is a relational data warehouse and, therefore, accepts only structured data types. Redshift requires some sort of management in the sense that, in times of high demand, if you need to scale, then you need to handle that manually by adding new nodes. Usage costs a minimum of $0.25 based on the type and number of nodes in your cluster. In general, Redshift may be an ideal choice for organizations that have already invested in AWS tooling and deployment for seamless integrations with other AWS offerings.

#Big Data #Databases #Relational Databases 26 social mentions