Data Integration

Learn about combining data from different data sources.

What is Data Sprawl?

Data sprawl refers to the overwhelming amount and variety of data produced by enterprises every day. With the growing number of operating systems, data warehouses, various BYOD devices, and enterprise and mobile applications, it’s no wonder that the proliferation of data is becoming a problem.

Read blog postRead case study

Data Mapping Tools

Learn about the various types of data mapping tools, including on-premise, open source, and cloud-based.

Read blog postRead case study

Worried about IoT? Think About Your Data Integration Plan

Trusting your IoT strategy to a cloud-based data integration solution like Alooma gives you a simple and secure method of collecting and combining all data from all sources into one location that can scale as your business needs change.

Read blog postRead case study

What is Data Integration?

Data integration involves combining data from different sources while providing a unified view of the combined data, enabling you to query and manipulate all of your data from a single interface and derive analytics and statistics.

Read blog postRead case study

Data Integration vs. Data Pipeline — What's the Difference?

If you're not currently in the middle of a data integration project, or even if just you want to know more about combining data from disparate sources — the first step is understanding the difference between a data pipeline and data integration.

Read blog postRead case study

The Importance of Data Integration to Today’s Business

Consolidating data to a central repository enables teams across the organization to improve performance measurement, gain deeper insights and actionable intelligence, and make more informed decisions to support organizational objectives.

Read blog postRead case study

Data Integration Tools

Learn about the various types of data integration tools, including on-premise, open source, and cloud-based.

Read blog postRead case study

MySQL to Google BigQuery Replication

A major reason to replicate your MySQL data into Google BigQuery, is the ability to join multiple data sources for valuable analysis. However, there are three approaches each with their advantages and disadvantages. Here, we’ll focus on our preferred method - Binlog Replication and show you the steps to do it.

Read blog postRead case study

The easiest way to load a CSV into Google BigQuery

BigQuery, Google’s data warehouse as a service, is growing in popularity as an alternative to Amazon Redshift. If you’re considering working with BigQuery, you’ll find that accessing the data is quite straightforward.

Read blog postRead case study

Six pitfalls when connecting Elasticsearch to Redshift

Redshift and Elasticsearch have a very different structure, as they are two very different solutions for data storage. While Elasticsearch is a full-text search engine based around schema-free JSON documents, Redshift is an SQL-based, columnar, schema’d data warehouse based on PostgreSQL.

Read blog postRead case study

Use webhooks to integrate data sources with Alooma

Use webhooks to integrate any SaaS product to your data warehouse

Read blog postRead case study

MySQL to Amazon Redshift Replication

How to replicate your MySQL to Amazon Redshift: A comparison of 3 common approaches with detailed real-world examples.

Read blog postRead case study

Export Application Insights to Amazon Redshift with Alooma

Learn how to export Application Insights data to Amazon Redshift with Alooma

Read blog postRead case study