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Data migration can be a time-consuming and error-prone process. But with the right data migration tool, you can automate the migration workflows, ensure high data quality and save a lot of that precious time for more value-generating tasks. In this blog, we'll cover 7 of the best data migration tools available on the market today, along with their pros, cons, and ideal applications.

Running a Shopify ecommerce store can be challenging - you have to take care of marketing, supplies, deliveries, returns, complaints, and every little thing in between, so you easily lose focus on what matters and brings value. Luckily, we have an easy solution! Keboola and Snowflake have partnered to bring you a no-code solution that turns your Shopify data into an insights machine in just 45 minutes. Let's take a look and try it hands-on! You might say.

Microsoft Power BI is a fantastic tool for data visualization and business intelligence. But as any data analyst can attest, before you can build metrics and dashboards, you need to spend more than 1/3 of your time preparing and cleaning the dataset for your Power BI instance. Luckily, there is a better way to cut down on this time-consuming task. In this article you will learn.

Who doesn’t love Apache Airflow? The Python-based open-source tool allows us to schedule and automate workflows with DAGs (Directed Acyclic Graphs). Data teams use Airflow for a myriad of use cases: from building ETL data pipelines to launching machine learning apps. The open-source tool makes workflow management easy: it is extensible, easy to monitor from the intuitive user interface in real time, and it allows you to build dependencies between jobs.

In a fast-paced world that produces more data than it can ingest, the right Python ETL tool makes all the difference. But not all Python tools are made the same. Some Python ETL tools are great for writing parallel load jobs for data warehousing, others are specialized for unstructured data extraction. In this article, we’ll explore the 7 best tools for ETL tasks and what business requirements they help you fulfill: Let’s dive right into the best tools and see how they compare.

Are you tired of sifting through mountains of data from different data sources before you find the data set you need to get your job done? It's time to simplify your life with data consolidation.

As every data engineer and analyst can attest, generating reports is one of the most time-consuming and human error prone activities in the day-to-day life of data analysts. Luckily, with the development of technology, data reporting can now be done automatically, which saves you time and reduces mistakes. In this article, you will learn.

Many businesses find it hard to use data to make business decisions, even though data is becoming an increasingly valuable asset for driving business growth. The data maturity model can help you identify the gaps in your data strategy that are stopping you from reaching a high level of data maturity. In this article, you will learn.

From airline tickets going through the roof during holiday seasons to Uber and other ride-sharing services charging higher prices in rush hour, we have become accustomed to paying different prices for the same services. Traditionally, dynamic pricing was a tool reserved for industry giants like Amazon because of its implementation complexity and price tag.

With a clear framework, best practices, and case studies. Modern enterprises are struggling with an overabundance of raw data and underutilization of data assets for achieving business objectives. The right data strategy helps you unlock the hidden potential from the stored-but-seldom-used enterprise data. In this article, you will learn.