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Why optimize your warehouse with a data lakehouse strategy

IBM Journey to AI blog

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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Db2 Warehouse delivers 4x faster query performance than previously, while cutting storage costs by 34x

IBM Journey to AI blog

Data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics that enable faster decision making and insights.

Big Data 182
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Data architecture strategy for data quality

IBM Journey to AI blog

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

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A beginner tale of Data Science

Becoming Human

Data Science You heard this term most of the time all over the internet, as well this is the most concerning topic for newbies who want to enter the world of data but don’t know the actual meaning of it. I’m not saying those are incorrect or wrong even though every article has its mindset behind the term ‘ Data Science ’.

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What is IT operations analytics?

IBM Journey to AI blog

IT operations analytics (ITOA) vs. observability ITOA and observability share a common goal of using IT operations data to track and analyze how a system is performing to improve operational efficiency and effectiveness. It aims to understand what’s happening within a system by studying external data.

DevOps 188
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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

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IBM Planning Analytics: The scalable solution for enterprise growth

IBM Journey to AI blog

Performance benchmarks Our in-memory TM1 engine rapidly analyzes big data, delivering real-time insights and AI-powered forecasting for faster, more accurate planning. Data updates are processed instantly, reflecting changes in real time and handling millions of rows per second, so decision-makers have up-to-date information.

Big Data 100