Mindex Cloud Services
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Data Ingestion

The First Step in Your Data Journey

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Embark on Your Data Journey

Step One - Data Ingestion

Ingestion

Step 1

Data Ingestion

Data Storage Icon

Step 2

Data Storage

Analytics

Step 3

Analytics

Data ingestion is the first step in the data pipeline, where data from diverse sources such as databases, streaming platforms, and external APIs, is collected and ingested into AWS services for storage, processing, and analysis, laying the foundation for informed decision-making. 
Here's how we can assist you in your data ingestion journey:  

  1. Identifying Data Sources: Whether your data is coming from databases, IoT devices, logs, or files from third parties, we'll help you identify all potential data sources.

  2. Structuring Data: We'll assess whether your data is structured or unstructured and determine its current format.

  3. Managing Data Volume: Understanding the size of your data is essential. We'll analyze whether it's measured in gigabytes, terabytes, or even petabytes.

  4. Evaluating Data Growth: We'll evaluate the rate at which your data is growing to anticipate future needs accurately.

  5. Tracking Data Changes: We'll examine whether your current source systems effectively track data changes to ensure data integrity and reliability.

 

Work with Mindex

Ready to get started?

Engage our cloud data team for a Complimentary Data Architecture Review led by a Certified AWS Data Architect.

In this one-hour session, we'll review your data pipeline's key pillars: Data Ingestion, Storage, and Analytics (AI/ML, Business Intelligence). Our goal is to identify challenges, opportunities, and establish a long-term data strategy, outlining next steps to enhance your data, analytics, and AI journey.

Start a Data Architecture Review

 

Dive Deeper into Data Ingestion

Explore our latest blog posts to assist you on your data journey!
2 min read

Laying the Groundwork: The Importance of Data Ingestion in Building a Strong Data Foundation

Apr 10, 2024 by Mindex

Your company's data is one of the most important assets it owns. With the rise of artificial intelligence (AI) and...
5 min read

Data Ingestion Nightmares: Common Pitfalls and How to Avoid Them for Better Data Quality Control

Sep 29, 2023 by Mindex

Managing increasing data is crucial for organizations in the era of big data. Data ingestion is an important step in...
4 min read

The Power of Data Lakes: Unlocking Business Insights You Never Knew Existed

Mar 1, 2023 by Mindex

Are you leveraging the full potential of your data? As analytics and big data become more commonplace, businesses need...

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AWS Purpose Built Analytics

Serverless and Easy To Use

AWS has the most serverless options for your data analytics in the cloud, including options for data warehousing, big data analytics, real-time data, data integration, and more. AWS manages your organization's underlying infrastructure so you can focus solely on your application.

Machine Learning (ML) Integration

AWS analytics services leverage proven machine learning (ML) and natural language capabilities to help you gain deeper and faster insights from your organization's data.

Ingest Data from Any Source

The AWS Cloud enables customers to overcome the challenge of connecting to and extracting data from APIs, streaming data, on-prem databases, or file-based sources in order to aggregate and analyze your data at near infinite scale. 

Gain Insights from your Data

AWS analytics services offer a range of analytics use cases, including interactive analysis, big data processing, data warehousing, real-time analytics, operational analytics, dashboards, and visualizations. 

By leveraging data-driven real-time analytics instead of intuition or guesswork, you can make more informed decisions.

Scalable Data Lakes

AWS-powered data lakes, supported by the unmatched availability of Amazon S3, can handle the scale, agility, and flexibility required to combine different data and analytics approaches. Build and store your data lakes on AWS to gain deeper insights than traditional data silos and data warehouses allow.