Centralize all your company’s growth marketing channels’ data into one place for easy analysis.

With Databox, Blerter moved from an entirely manual process of tracking data to one that allows them to set and track goals, benchmark progress, and ultimately make improvements.

Learn about ETL and ELT so you can decide which method works for you.

The key differences between Stitch, Jitterbit, and Xplenty: The average business pulls data from 400 different locations, which makes it tricky to generate valuable data insights. Data-driven organizations use an Extract, Transform, and Load (ETL) platform to pull all this information into a data lake or warehouse for deeper analysis. However, many businesses lack the technical skills (like coding) to facilitate this process. The three tools in this review make ETL workflows easier.

Natural language generation (NLG) is best described as a sub-type of artificial intelligence (AI) that generates linguistically rich descriptions of insights, both written and spoken, in plain English. It does this by automatically scanning and finding the most interesting and important concepts in structured data that resides in our databases or apps, and translating it into a consumable, text-based narrative that is easier for the average business user to access and understand.

Snowflake and Saturn Cloud are thrilled to announce our partnership to provide the fastest data science and machine learning (ML) platform. Snowflake’s Data Cloud comprises a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Saturn Cloud’s platform provides lightning-fast data science. Combined, our solutions enable customers to maximize their ML and data science initiatives.

On December 8th, it was time for the annual “State of the Union” from Qlik, with regards to BI & Data Trends. Overwhelmingly, attendance was in the many thousands, and we received thousands of questions. To get that type of engagement in a year where people have done nothing but virtual conferences is amazing. One person put it to me like this: “I just joined in on your webinar on the top data and analytics trends and it was truly fantastic.

The problem with modern analytics is that it overpromises and underdelivers. We can even quantify the disappointment: This begs the question: why even bother with analytics? Well, when analytics is done right, it pays back $13.01 for every dollar spent. In fact, data-driven companies outperform their competitors in almost every conceivable way. We have broached the topic of extracting value from data before, from how to set up the right data strategy to building a data-driven culture.

Introduction Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. Apache HBase is an effective data storage system for many workflows but accessing this data specifically through Python can be a struggle. For data professionals that want to make use of data stored in HBase the recent upstream project “hbase-connectors” can be used with PySpark for basic operations.

How large is your Hadoop data lake? 500 terabytes? A petabyte? Even more? And it is certainly growing, bit by bit, day after day. What began as inexpensive big data infrastructure now demands ever more expenditures on storage and servers while becoming increasingly unwieldy and expensive to manage. Such rapacity makes it ever harder to realize a proper return on investment from that Hadoop infrastructure.