Mistake7: The budget, the budget
Posted: Mon Jan 27, 2025 7:22 am
Mistake No. 6: Not defining a business goal
Hadoop projects are complex. To better understand when, where and for what you are actually using it, it is important to get input from everyone involved in the project from the start. The first questions to ask are what business goal you want to achieve, who will benefit from the investment and how you can justify the expenditure. Most big data projects fail because they do not deliver the expected added value. To correctly calculate the added value, you have to start with the data problem you want to solve.
Do we need big data now or in the future? Do we need self-service data preparation? Do the analysis functions need to be embedded in other applications or portals? Does data preparation take longer or does visualization take longer? Answering these questions helps to determine whether the current architecture might be sufficient to achieve the business goals or whether a new architecture is needed.
One of the reasons why benin telegram screening companies are turning to Hadoop is price. Hadoop is relatively inexpensive and can be scaled cost-effectively. However, many overlook the hidden costs associated with Hadoop when planning, such as replication/compression costs, expertise costs, and data integration management costs. Hadoop was designed to handle a huge amount of data that is not only diverse but also growing quickly. In terms of replication, however, this also means that with a standard setting of triple replication (for the sake of simplicity, we're not considering compression of the various file formats in HDFS (Hadoop Distributed File System)), 1TB of data will require at least 3TB of storage. Another important factor for sizing is the type of use. If a simple data store is desired, low processing power and large hard drives are enough. However, if you plan to use machine learning or in-memory technologies (keyword " big compute "), the hardware requirements are logically much higher, but the added value is usually higher too.
Hadoop projects are complex. To better understand when, where and for what you are actually using it, it is important to get input from everyone involved in the project from the start. The first questions to ask are what business goal you want to achieve, who will benefit from the investment and how you can justify the expenditure. Most big data projects fail because they do not deliver the expected added value. To correctly calculate the added value, you have to start with the data problem you want to solve.
Do we need big data now or in the future? Do we need self-service data preparation? Do the analysis functions need to be embedded in other applications or portals? Does data preparation take longer or does visualization take longer? Answering these questions helps to determine whether the current architecture might be sufficient to achieve the business goals or whether a new architecture is needed.
One of the reasons why benin telegram screening companies are turning to Hadoop is price. Hadoop is relatively inexpensive and can be scaled cost-effectively. However, many overlook the hidden costs associated with Hadoop when planning, such as replication/compression costs, expertise costs, and data integration management costs. Hadoop was designed to handle a huge amount of data that is not only diverse but also growing quickly. In terms of replication, however, this also means that with a standard setting of triple replication (for the sake of simplicity, we're not considering compression of the various file formats in HDFS (Hadoop Distributed File System)), 1TB of data will require at least 3TB of storage. Another important factor for sizing is the type of use. If a simple data store is desired, low processing power and large hard drives are enough. However, if you plan to use machine learning or in-memory technologies (keyword " big compute "), the hardware requirements are logically much higher, but the added value is usually higher too.