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Data Lakes

Introducing Databricks Support: Operational AI for the Lakehouse

On the heels of announcing our $14.5M Series A and General Availability, we’re excited to be at the Data + AI Summit to unveil support for Continual on the Databricks Lakehouse. Increasingly, data and ML tool providers are embracing a data-centric approach to the ML workflow. The goal is to focus on what increasing drives ML – the data – compared to infrastructure, algorithms, or pipelines. At Continual we bet on data-centric AI from day one.

The Future of the Data Lakehouse - Open

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Snowflake's Newest Workload for the Data Cloud: Cybersecurity

Cybersecurity is a data problem at its core. Yet, security teams haven’t achieved tremendous success in utilizing the modern data stack that data analytics teams have enjoyed for years. Security teams face constant pressure from vulnerabilities and breaches in their infrastructure and supply chains because they remain on a proverbial island with antiquated technology. Cybersecurity leaders must uplevel their strategies by implementing a modern security data lake.

Optimize Your AWS Data Lake with Data Enrichment and Smart Pipelines

As an engaged member of the AWS community, we’re always on the lookout for new technologies and software tools that can help our customers succeed in their AWS data lake initiatives. During the most recent AWS Re:Invent conference in Las Vegas, we had the opportunity to engage directly with AWS partners, customers, and other technology companies operating in the AWS ecosystem.

Data Legends Podcast: Musings on Data Lakes, Computer Science, AI & More

When it comes to building new products, there’s a fine line between which pieces of the puzzle should be owned by humans with deep domain knowledge, and which aspects can or should be automated through AI. How far can the boundary be pushed? We speak with Jeremy Foran, Chief Technology Officer at Purple Cow Internet, about his new role as CTO at a fast-growing internet service provider.

Data Lake vs Data Warehouse: 7 Critical Differences

Here are seven key differences between data lakes vs data warehouses: A lot of terms get thrown around in the big data space that every business should understand. Many of these terms are easily confused with each other. This is the case with data lakes vs data warehouses. What are some of the most important differences between them, and how can your business use them most effectively for data analytics and data management? Read on to learn the differences between data lakes and data warehouses.

Building and Managing the Modern Datastore: The Data Lakehouse

The 'data lakehouse' is quickly becoming popular in the data analytics community. Data lakehouse architecture combines the benefits of a data warehouse and a data lake. It aims to merge the data warehouse’s data structure and management features along with the flexibility and relatively low cost of the data lake. Watch this panel discussion to learn how the data lakehouse can address the limitations of the data lake and data warehouse architecture to deliver significant value for organizations. Explore why the data lakehouse is an ideal option for enterprise data storage initiatives.

FinTech Companies Thrive and Innovate with ChaosSearch

ChaosSearch addresses critical pain points and overcomes core operational challenges for FinTech companies, allowing them to accelerate application development and streamline their operations in the cloud. The ChaosSearch data lake platform delivers search and relational analytics at scale directly in Amazon S3, with no data movement, no ETL process, and zero administrative overhead.

Make Your AWS Data Lake Deliver with ChaosSearch (Webinar Highlights)

When CTO James Dixon coined the term “data lake” in 2011, he imagined a single storage repository where organizations could store both structured and unstructured data in their raw format until it was needed for analytics. But without the right storage technology, data governance, or analytical tools, the first data lakes quickly became “data swamps” - morasses of data with no organizational structure and no efficient way to access or extract meaningful insights.