Data

PROFESSIONAL SERVICES

Data Engineering and Analytics on AWS

Leverage the Power of Data & Analytics to Elevate Your Business

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OVERVIEW

Turn Data Into Business Innovation and Growth

Join the growing number of companies that are turning to AWS data analytics to get actionable insights, make informed decisions, and capitalize on business opportunities. Data-driven organizations are three times more likely to report significant improvements in decision-making, and Mission can help you reap these benefits.

FASTTRACK

QuickSight FastTrack

Move away from cumbersome legacy BI tools to Amazon QuickSight with our QuickSight FastTrack. Our deeply experienced team delivers a seamless, efficient migration, empowering your organization with speed, clarity, and powerful insights.

Modernize your business intelligence by migrating to Amazon QuickSight. Reduce costs, improve performance, and empower your teams with self-service analytics.

Modernize Your BI

BENEFITS

Expert Guidance and Support When You Need It Most

As an AWS Data and Analytics Competency partner, Mission will help you successfully plan, design, and build a reliable and scalable data infrastructure to ingest, store, supplement, process, visualize, and analyze data in your AWS environment.

Team of AWS and Data Experts

Solve data challenges with a team of AWS cloud and data experts at a fraction of the cost of building an in-house team.

Tailored Data Solution for Your Needs

Customize your data and analytics approach to meet your specific use cases and objectives.

Navigate Build vs. Buy Decision

We take a consultative approach to determine which data solutions make the most sense for your business.

Visualize and Identify Patterns with BI

Build dashboards and gain insights with business insight tools such as Tableau, Amazon QuickSight, and Power BI.

Improve Data Governance

Monitor your data on an ongoing basis to ensure best practices for regulatory compliance.

Drive Data-Driven Decision Making

Detect anomalies in new data, recommend unique activities for customers and make better decisions.

FEATURES

Optimize Your Data Infrastructure and Movement on AWS

Our expertise can guide you through the whole process of building a data architecture: whether you’re establishing a data lake, lakehouse, data warehouse or data mart, or you’re modernizing an outdated and underperforming data pipeline. Transform your data pipelines into robust systems tuned and prepared for business analytics.

Take Advantage of Modernized Data Architecture, Strategy and Tools Faster and Cost Effectively

Data ingestion jobs (moving data into the data lake)
Design and build the base data lake (S3 buckets, security, networking, data governance, data lineage, permissions)
Create data pipelines (ETL jobs, transformations, connections, and tools)
Data warehouses/marts (Amazon Redshift, Snowflake, Amazon RDS)

Data lake customization and ad hoc projects involving the AWS Data Stack
Data ingestion jobs (moving data into the data lake)
AWS data modernization (move to AWS Data Infrastructure like Amazon RDS, Amazon Redshift or Amazon Aurora)
BI/Visualization tools (Tableau, Amazon QuickSight, PowerBI)

Mission enables us to focus on confidently delivering (and continuously enhancing) our business-facing products, and as little time as possible pushing buttons to scale AWS architecture or perform maintenance. Bringing on Mission has, and continues to, make a lot of sense for us.

Russell Wangler

Former CTO

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Data Engineering & Analytics Frequently Asked Questions

How can I architect a scalable and cost-effective data lake on AWS?

This is a major question covering several subjects. Start with your business outcome, what is it you are trying to achieve for your business through the use of data. After you determine the outcome, you need to think about data ingestion and ETL, along with how you plan to query and visualize. Services like AWS Glue and Amazon S3 can be critical, with tiered storage based on access frequency for cost, and you’ll want to consider how you will query, with services like Amazon Athena, and how you plan to visualize, like with Amazon QuickSight. You may want to segregate data by use case, to help your stakeholders stay organized, but as you can see it is key not to think of the data lake in isolation—understand where it stands in relation to your users and data sources.

How can I ensure data security and compliance throughout my analytics workflow on AWS?

For any compliant solution, you need to consider how you’re handling encryption at rest in services like Amazon S3, Amazon Redshift, and Amazon RDS, as well as encryption in transit. This is also about instituting correct IAM policies for protecting sensitive data access and governance. If you’re storing any personally-identifiable data, you may also want to consider adopting Amazon Macie to ensure it stays appropriately secured or scrubbed where necessary. Services like Amazon Comprehend can also be valuable for a redaction stage, if you need to hide sensitive data as part of an analytics workflow.

How can I build a serverless analytics pipeline on AWS?

You need to design your architecture to leverage services like AWS Lambda while considering issues of state management and design for processing logic. Amazon S3 obviously provides serverless benefits, as does AWS Glue, so we often recommend these in conjunction. We may also suggest you consider serverless options for data querying like Amazon Athena and Amazon Redshift.

How should I manage cataloging and data discovery when dealing with multiple sources?

Centralizing metadata and having a unified cataloging strategy is crucial to ensure that data is discoverable, its lineage is traceable, and stakeholders across the organization can find and utilize what they need in an efficient manner. AWS Glue is powerful for data cataloging and discovery, and for querying across these datasets you may want to consider tools like Amazon Athena or Amazon Redshift Spectrum.

We’re working with our data on AWS but we’re unhappy with performance. Can you fix this?

We work with teams at all levels of data sophistication and this includes helping to refine and manage AI, ML, and Data Operations. We can analyze your data architecture for root causes and help you automate operations, like your model and data pipelines.