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In financial services, data has always been viewed as a strategic asset. To manage this data, organizations have invested heavily over several years and across a number of technology generations in the underlying data infrastructure. This approach has left a large data technology legacy along with silos of data linked to specific infrastructure and applications.

On the surface, Talend seems like the ultimate data integration platform. It's open-source, maintains multi-cloud integration, supports data governance frameworks like GDPR and CCPA, and handles both ETL and ELT, providing you with more flexibility for data management. Dig a little deeper, though, and you'll notice this platform has an outdated user interface and limited capabilities, and you'll probably need to upgrade to its enterprise version to execute data integration.

In a recent webinar by TDWI, 45% of analysts reported that “every day seems to be a different fire drill.” No surprise to anyone in the industry. As much as analysts need to be focused on more strategic tasks, their skills are frequently deployed to answer basic questions. Greater self-service capabilities for end-users would no doubt alleviate these fire drills, but this is not yet a reality for the majority of companies.

From increasing brand awareness to hashtag research and displaying social proof at scale, here are 12 ways SMBs can leverage Instagram for marketing.

In our modern digital society, data is abundant, and storage is affordable. Businesses, governments and even individuals can (and do) collect every transaction, click, swipe, location, message and attribute in their datasets. With just a few clicks on my smart device, I can review data on every place I’ve been, how much I spent, every step I took, what the weather was like and who I was with. Businesses collect the same abundance of data.

One of the most substantial big data workloads over the past fifteen years has been in the domain of telecom network analytics. Where does it stand today? What are its current challenges and opportunities? In a sense, there have been three phases of network analytics: the first was an appliance based monitoring phase; the second was an open-source expansion phase; and the third – that we are in right now – is a hybrid-data-cloud and governance phase. Let’s examine how we got here.

Apache Hive and Apache HBase are incredible tools for Big Data. While there is some overlap in their functions, both Apache Hive and Apache HBase have unique qualities that make them better suited to specific tasks. Some key differences include: Ultimately, comparing Apache Hive to Apache HBase is like comparing apples to oranges or Google to Facebook. While the two entities are similar, they don't provide users with the same functionality.

BigQuery is a fully-managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and intelligent caching for business intelligence. To help you make the most of BigQuery, we’re offering the following no cost, on-demand training opportunities.

Run Fivetran on different clouds to gain flexibility and control while reducing costs.