data lakes explained
Why use data lake?
The term "data lake" describes more than data storage, raw data, or a data management model. This goes beyond how data is stored; it relfects an architecture that allows you to store different types of data, large or small, in an accessible and cost-effective architecture.
A fundamental tenet of this architecture is data accessibility. Accessibility is the foundation of an agile data lake pattern. For data science or business users, accessibility means they can run different types of processes from visualizations, real-time analytics, transformations, machine learning, and many other functions as needed.
The Openbridge lake formation service offers business or enterprise data teams the ability to harness more data, from more sources, in less time, and at a lower cost. Openbridge embraces leading data lake trends where technology is used to deliver new abilities to analyze data sets in different ways leads to better, faster decision making.
Ready-to-go data lake data ingestion
Kickstart your projects with ready-to-go automated data pipelines to your data lake or warehouse. Openbridge helps you break down external and internal data silos.
The Openbridge data pipeline platform is a code-free, zero administration service that deliver data to highly available, cost-effective data lakes using Azure Data Lake, Amazon Athena or Redshift Spectrum.
Over 500 data sources to free data scientists and analysts from painful data wrangling. Openbridge automation unlocks the hidden potential of data for machine learning, business intelligence, data modeling, or online analytical processing.
data lake case study
Accelerate organizational productivity
An Aberdeen survey found organizations that implemented a lake beating similar companies by 9% in organic revenue growth. The data lake advantages afforded the ability to undertake machine learning over new sources like log files, click-streams, social media, and internet-connected devices.
Bain & Company found that leaders own their digital destiny by taking control of their consumer data and by integrating their marketing and advertising technologies. These leaders in North America are 1.6 times likelier than laggards to prioritize integrating their platforms, and they have a stronger understanding of how to deploy their technology.
Leaders who are integrating technologies for automated data pipelines shortened the time to insights. The Openbridge service can minimize technical debt while accelerating an enterprise's consumption of data. Given an accelerating rate of change in a cloud warehouse, query engine, and data analytics market, minimizing risk and technical debt are a core part of the Openbridge data lake solution strategy and architecture.
Zero admin, code-free data ingestion pipelines to Azure or Amazon data lakes
Openbridge delivers simple, reliable, and easily integrated data ingestion solution for Azure and AWS data lakes.START FREE TRIAL
Own your data
Take control of your own data. Unlock data silos securely into your private data lake or cloud warehouse technologies
Choose your tools
Get answers quickly using the skills you have today using preferred BI, analytics, or reporting tools
Code-free, automated integrations collect, organize, and catalog data fueling insights that accelerate growth and profitability
data lake terminology
Types of data lakes
A data lake has a natural state, often reflecting ecosystems of data, just like those in nature reflect ecosystems of fish, birds, or other organisms. Here are a few types enterprises employ;
- The Great “Caspian”: Just like the Caspian is a large body of water, this type of lake is a large, broad repository of semi-structured and unstructured data.
- Temporary “Ephemeral”: Just like deserts can have small, temporary lakes, an Ephemeral lakes exist for a short period. They may be used for a project, pilot, PoC, or a point solution.
- Domain “Project”: These types of lakes, like Ephemeral, are often focused on specific knowledge domains like sales or marketing. However, unlike the Ephemeral lake, this lake persists over time.
Large, small, or anything in between, Openbridge supports an architecture that fits your needs.
"Openbridge showed us how we can leverage new opportunities with data lakes. They helped us realize a cost-efficient, cross-cloud AWS data lake to Oracle architecture. We’re now tapping into a modern and innovative data stack with the help of the Openbridge plaform."
Query your data, server free
Serverless query engines deliver on-demand value
Serverless, interactive query services make it easy to analyze data in your lake using standard SQL. With query engines like Amazon Athena or Redshift Spectrum, there is no infrastructure to manage, and you only pay for the queries that you run.
With the Openbridge lake service, there’s no need for complex ETL jobs to pipeline to your lake. We provide an out-of-the-box data catalog, creating a zero administration, fully-automated metadata repository of schemas, tables, partitioning, and compression. Our service makes it easy for anyone to get up and running quickly with a lake.
How to query a data lake?
What is data lake analytics? Freedom from vendor lock-in
A principal cloud data lake or on-premise data lake design consideration is supporting a broad array of industry-leading BI tools. Pairing your solution with an open-source, standards-based distributed SQL query engine gives you the flexibility to undertake analysis with your preferred tools.
An open, on-demand data lake strategy means you can run queries directly against your raw structured and unstructured data from a wide variety of tools like; Tableau, Microsoft Power BI, Looker, Amazon Quicksight, and many others.
The Openbridge service ensures you have the data you need ready to fuel the tools you love.
Control access and authorizations
Data lake security
The Openbridge data lake solution architecture uses a central data catalog. A catalog allows you to set access controls for a layer of data lake security and data governance. You define the rules at the table and column-level for users of Redshift Spectrum and Amazon Athena or an Azure Data Lake.
The Openbridge data catalog service works behind the scenes as a code-free, fully-managed service. Our service offers an easy-to-use and straightforward setup process that provides a powerful cataloging system for capturing metadata and reflects changes to your lake or cloud warehouse. Our approach empowers organizations to focus on using data, not wrangling it.
"We have enough to worry about, so relying on Openbridge as a data and technology partner is critical for us. They delivered the technology and tools that allow our analytics and executive teams to work from a unified data lake environment for business insights.
Reduce costs, distribute workloads
Optimize your cloud data warehouse investments
Postgres, MySQL, Redshift, Oracle, or SQL Server users can benefit from offloading tasks and data to a lake. For example, Redshift users can leverage Redshift Spectrum to offload large datasets reducing the need to add capacity to your cluster. Using Openbridge data migration services, you can offload data and use query engines like Redshift Spectrum and Amazon Athena or an Azure Data Lake .
Query data in place
Extend your architecture with Athena federated queries
AWS Athena federated query services open new pathways to query "in situ" data with your current lake implementation. Federated queries enable business users, data scientists, and data analysts the ability to run queries across RDBMS, NoSQL, and custom data sources right from Athena. A user can submit a SQL query that can get executed across multiple sources in place.
Data lake vs data warehouse, which is better?
Avoiding the data warehouse vs data lake myths
It is not uncommon to see a data lake framed as just "storage" or claims are made that a data warehouse is a lake. As a result, this narrow dialogue gets stuck on consultant or vendor talking points that artificially pits both models against each other.
The innovative, strategic discussion, is how a lake and warehouse are designed to work in tandem. Specific jobs and tasks, traditionally done in a warehouse, can be offloaded to a data lake for cost efficiencies. Pairing the two can ensure flexibility across both engineering and business capabilities.
Ultimately, the warehouse and data lake service model are about delivering business value.