Data integration resources and best practices

What is Data Integrity?

Data integrity is the assurance of accuracy and consistency of data over the course of the data life cycle (from when the data is recorded until it is destroyed).

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What is Data Mapping?

Data mapping is a necessary component of the larger processes of data migration and data integration. It’s a mechanism that matches fields from data sources (system A) to the target fields in a data warehouse or other storage repository (system B).

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What is Data Consolidation?

Data consolidation is the process of combining all of your data wherever it may live, removing any redundancies, and cleaning up any errors before it gets stored in one location, like a data warehouse or data lake.

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Now That IoT Is Everywhere, What Do You Do About It?

Learn about what gets in the way of effectively mobilizing your data and what you can do about it to embrace IoT with greater confidence.

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What are Data Silos?

A data silo is a collection of information in an organization that is isolated from and not accessible by other parts of the organization. Removing data silos can help you get the right information at the right time so you can make good decisions.

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What is Data Redundancy?

Data redundancy occurs when the same piece of data is stored in two or more separate places. In this article you'll learn all about data redundancy, including how it occurs, why it's a problem, and how a modern data pipeline can reduce it.

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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.

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Data Mapping Tools

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

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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.

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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.

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What is Data Cleansing?

The goal of data cleansing is to improve data quality and utility by catching and correcting errors before it is transferred to a target database or data warehouse.

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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.

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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.

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Data Integration Tools

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

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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.

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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.

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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.

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