{"id":4904,"date":"2022-08-22T17:20:33","date_gmt":"2022-08-22T21:20:33","guid":{"rendered":"https:\/\/solutionsreview.com\/data-integration\/?p=4904"},"modified":"2022-10-07T13:41:45","modified_gmt":"2022-10-07T17:41:45","slug":"an-introduction-to-edge-to-cloud-data-pipeline-components","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-integration\/an-introduction-to-edge-to-cloud-data-pipeline-components\/","title":{"rendered":"An Introduction to Edge to Cloud Data Pipeline Components"},"content":{"rendered":"

\"Edge<\/p>\n

This is part of Solutions Review\u2019s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, CTERA<\/a> CTO Aron Brand offers an introduction to edge to cloud data pipeline components.<\/em><\/strong><\/p>\n

\"SRLooking to get ahead of the competition? Edge-to-cloud data pipelines can provide the data-driven edge needed to succeed. If you look at any organization, whether it\u2019s a for-profit business, a nonprofit, or a government agency, they\u2019re all trying to become more data-driven for one simple reason: it\u2019s the best way to make decisions.<\/p>\n

In the past, organizations would make decisions based on gut instinct, or on what the boss said. But that\u2019s no longer good enough. With the advent of big data and powerful data analysis tools<\/a><\/strong><\/span>, organizations can now make decisions based on hard evidence.<\/p>\n

Data-driven decision-making has a number of advantages. First, it leads to better decisions. Second, it allows for making decisions more quickly. Third, it\u2019s more transparent, because everyone can see the data that went into the decision. The problem is that most organizations are not very good at data-driven decision-making.<\/p>\n

Why? Because they don\u2019t have the right data where it\u2019s needed.<\/p>\n

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The challenge is that in the enterprise, 75 percent of data is created at the edge – in branch offices, on mobile devices, and by remote smart devices. How do you quickly and easily access, analyze, and draw conclusions from data that is spread across hundreds of silos? You need to aggregate all this data in one place. What you need is a data lake – a centralized, curated, and secured repository that allows you to collect all your data, both structured and unstructured<\/a><\/strong><\/span> from across your organization, and make it available for analytics and data processing<\/a><\/strong><\/span>.<\/p>\n

The key to success with data lakes is to make sure that they are updated continuously so that the data is timely and not stale. This means that you need to have a way to collect data from all of your silos and then ingest it into your data lake \u2013 and in a timely manner.<\/p>\n

In simple terms, you may have plenty of data, but like milk, your data has an expiration date. To extract the most value from your data, you need to be able to collect it quickly, store it efficiently, and analyze it effectively. Otherwise, your data will go bad before you can use it to make informed decisions.<\/p>\n

So, in addition to having a data lake, you need to add two key pieces of the puzzle, the first being a way to ingest data quickly so that it can be available for analysis as soon as possible.<\/p>\n

The second key piece of the puzzle is to be able to react to the data that you have ingested, quickly enough to be useful. This means having the ability to analyze the data and then take action based on what you have learned.<\/p>\n

An edge-to-cloud data pipeline can provide these missing pieces of the puzzle. Edge-to-cloud data pipelines provide an efficient way to manage data that is generated at the edge. By automating the ingestion, preparation, and management of data, these pipelines enable organizations to receive actionable intelligence in real-time.<\/p>\n

A typical edge-to-cloud data pipeline consists of three components:<\/p>\n