Data Management with Master Data Catalogues and Lakehouses
Have you ever stopped to think about just how much data we produce every day? Between all of our smart watches, phones, emails, apps, videos, web searches, documents, etc., we created 74 zettabytes (1 zettabyte [ZB] = 1,099,511,627, 776 gigabytes [GB]) per day in 2021. And that number is set to reach 149 zettabytes a day by 2024¹. That’s a lot of data! Now, let’s consider mission-critical business data. What is the best way for your business to manage its data so that you can better study, model and use it to drive growth?
Many Alithya clients build a data warehouse only once they have a clear vision of what they want to do with the data. But what if you’re not yet sure how you want to use the data? A data lake is an inexpensive way to collect and store massive amounts of data that you can then later move to a data warehouse for analysis. Data lakes are storage repositories that hold large amounts of data in its native, raw format. They are optimized for scaling to terabytes and petabytes of data.
Many clients use both a data warehouse and a data lake, which together form a lakehouse. Data lakehouses combine the reliability and structure of data warehouses with the scalability and agility of data lakes. Lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse directly on top of low-cost cloud storage in the open formats of a data lake.
All of the information about the data is stored and organized in a centralized location called the master data catalog. This catalog helps enterprises access their data and avoid duplicate/redundant data products when ingesting data from different project teams. We recommend you use a data catalog service to define the metadata of the data products stored in the various data landing zones.
At Alithya, we devise a data strategy while working alongside our clients. We assess the client’s needs, analyze their data and determine the best roadmap. Our clients often want to consolidate and aggregate data from various business tools, including CRMs, ERPs and project management tools in order to have a 360-degree view of their business. It’s also important to define an effective data governance strategy to ensure that the data you use in your business operations, reports and analysis is discoverable, accurate, trusted and can be protected.
Depending on the client’s needs, we can leverage our partnerships with Amazon Web Services, Microsoft, Snowflake, or a combination of all 3, to set up cloud data management.
Microsoft Azure
Alithya is a Gold Certified partner with more than 20 Microsoft awards and over 13 Microsoft Gold Certifications, including one for data analytics.
Microsoft Azure has several analytics solutions including Synapse, Purview, Data Factory, Azure ML and Power BI. These tools can help visualize data, share insights across an organization and embed them into a variety of platforms to share with an audience. Together, Azure and Power BI provide insights at scale, allowing businesses to develop the data-driven culture needed to thrive in a fast-paced, competitive environment.
Amazon Web Services (AWS)
With more than 50 accredited certifications, Alithya is an Advanced AWS Partner. AWS offers multiple services for secure, flexible and cost-effective data management.
AWS provides a range of web services (e.g., Glue, Redshift, Quicksight, Athena Kinesis, OpenSearch, Database Migration Service [DMS]) as well as partner solutions to help ingest and migrate data from the cloud and on-premise sources into Simple Storage Service (S3). AWS also offers several fully managed analytics services like Elasticsearch and Athena to help analyze log data and run interactive queries.
Snowflake Cloud Data Platform
Alithya is a Select Snowflake Partner.
Built on a flexible platform, Snowflake provides the scalability, elasticity and low-cost storage of a lake, along with the security, governance and performance of a warehouse. Available in AWS and Azure, Snowflake allows you to load a diverse array of data in its native format, without having to transform it, giving you the flexibility and agility of a data lake. Users can also leverage Snowflake’s Massively Parallel Processing architecture to spin up multiple virtual warehouses and run several queries at the same time. Snowflake lets you share data with partner tools like Apache Spark, using ODBC and JDBC connectors for real-time large-scale data processing.
Want to learn more about how your business can leverage data transformation and management? Contact us for a consultation with one of our BI experts.
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