A method for detecting anomalies in a data pipeline, wherein a Reader extracts data tables from a Source Dataset at a node of the data pipeline, the method comprising the steps of: - receiving by a Table Analyser from the Reader a first data table; - retaining by the Table Analyser a column of the first data table with a time dimension as a candidate time column, and forming a first sequence of points, each point having as one dimension a time interval, and as another dimension a count of data lines per time interval in the first data table; - forming by the Table Analyser a second sequence of M points by setting aside points from the first sequence for a first number of time intervals immediately preceding the last time interval; - providing by the Table Analyser the second sequence to a Breakpoint Finder; - calculating by the Breakpoint Finder a first regression for the second sequence, and receiving by the Table Analyser the first regression for the second sequence; - applying by the Table Analyser the first regression on the time intervals of the set aside points, obtaining first predicted values for the corresponding counts of data lines, and calculating a first error representative of the difference between the first predicted values and the actual values for the counts of data lines for each of the time intervals of the set aside points; - repeating by the Table Analyser the retaining, forming, providing, receiving and applying, on columns in the first data table having a time dimension, and retaining as the best time column for the first data table the candidate column yielding the smallest calculated first error; - storing by the Table Analyser the first sequence for the best time column in a Data table; - receiving by a Table Aggregator from the Reader the first data table updated with at least one additional time interval, and corresponding count of data lines; - forming by the Table Aggregator a third sequence of P points, each point having as one dimension the time interval, and as another dimension a count of data lines per time interval in the updated first data table; - storing by the Table Aggregator the third sequence in the Data table, as a substitute for the first sequence; - calculating by an Anomaly Detector for a first selection of time intervals in the third sequence a confidence interval with a center being the value for the count of data lines predicted by a regression received from the Trainer; and - raising by the Anomaly Detector an anomaly if the actual value for the count of data lines for any time interval of the first selection lies outside the confidence interval. - receiving by a Table Analyser from the Reader a first data table; - retaining by the Table Analyser a column of the first data table with a time dimension as a candidate time column, and forming a first sequence of points, each point having as one dimension a time interval, and as another dimension a count of data lines per time interval in the first data table; - forming by the Table Analyser a second sequence of M points by setting aside points from the first sequence for a first number of time intervals immediately preceding the last time interval; - providing by the Table Analyser the second sequence to a Breakpoint Finder; - calculating by the Breakpoint Finder a first regression for the second sequence, and receiving by the Table Analyser the first regression for the second sequence; - applying by the Table Analyser the first regression on the time intervals of the set aside points, obtaining first predicted values for the corresponding counts of data lines, and calculating a first error representative of the difference between the first predicted values and the actual values for the counts of data lines for each of the time intervals of the set aside points; - repeating by the Table Analyser the retaining, forming, providing, receiving and applying, on columns in the first data table having a time dimension, and retaining as the best time column for the first data table the candidate column yielding the smallest calculated first error; - storing by the Table Analyser the first sequence for the best time column in a Data table; - receiving by a Table Aggregator from the Reader the first data table updated with at least one additional time interval, and corresponding count of data lines; - forming by the Table Aggregator a third sequence of P points, each point having as one dimension the time interval, and as another dimension a count of data lines per time interval in the updated first data table; - storing by the Table Aggregator the third sequence in the Data table, as a substitute for the first sequence; - calculating by an Anomaly Detector for a first selection of time intervals in the third sequence a confidence interval with a center being the value for the count of data lines predicted by a regression received from the Trainer; and - raising by the Anomaly Detector an anomaly if the actual value for the count of data lines for any time interval of the first selection lies outside the confidence interval.